esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Fly Ash Obtained from the Incineration of Municipal Solid Waste in Agriculture
## 1 Introduction  
The developments of the global economy and urbanization have led to increased amounts of waste worldwide [1; 2]. The daily amount of waste produced in cities is 1.2 kg/capita/day and annually 1.3 billion tons per year; 15% of the entire amount is incinerated [3]. Considering the large volume of waste and people's opposition to landfills due to environmental pollution, over time, researchers have sought solutions to the problem of municipal solid waste management. In this paper, we analyze the possibility that the ash obtained from the incineration of this waste can be used to remediate degraded soils due to the nutrients it contains (phosphorus, potassium, calcium, and magnesium), making it a fertilizer for increasing the yield of agricultural plants [4].  
MSW (municipal solid waste) can be processed by biological treatment, thermal treatment, or storage (Figure 1) [1; 5]. Through heat treatment, the mass of waste is reduced and biofuel is obtained. The decomposition of organic matter in waste by microbes is a biochemical treatment.  
Biochemical conversion is recommended in the case of waste with high moisture content and a high percentage of biodegradable organic matter. The residues obtained from these processes are stored [5]. Thermal treatment of waste is carried out by gasification, pyrolysis, and incineration [6]. Through gasification, waste is transformed into combustible gases by thermal decomposition in an oxygen-poor atmosphere to prevent complete combustion. Through pyrolysis, waste is chemically decomposed at high temperatures in the absence of air [5].  
By incinerating municipal solid waste, the fuel is burned in the furnace, where heat is produced and carried by the combustion gases at 800-1000 \({}^{\circ}\)C. The flue gases are cooled in the boiler with high-pressure water feed, where the steam is raised. This steam can be used to produce electricity [7].  
The high cost of landfills, lack of land availability, stricter regulations, and people's opposition to siting new landfills [6] that pollute the soil, groundwater, and air by generating methane gas from waste degradation have led to municipal waste management by incineration (MSWI) [8,9].  
MSWI has the following advantages: the volume of waste is reduced by 85-90% and the mass of waste is reduced by approximately 70%, thus avoiding land occupation; heat and electricity are generated; and waste is eliminated faster than through biological decomposition [10,11].
# Harnessing Biotechnology for the Remediation of Organic Pollutants in Coastal Marine Ecosystems
## 6 Critical Analysis of Phytoremediation vs. Microbial Remediation
### Limitations  
Each technique faces context-specific challenges. Bioremediation is sensitive to environmental fluctuations such as salinity, temperature, oxygen availability, and pH, which can suppress microbial metabolism or viability. Additionally, bioaugmentation with non-native microbes poses ecological risks, while biostimulation requires precise nutrient and environmental management. Phytoremediation faces limitations in terms of plant species selectivity, root depth, and pollutant specificity. It is often ineffective in degrading highly persistent compounds and may result in the accumulation of toxic intermediates in plant tissues or sediments. Furthermore, seasonal growth patterns and biomass harvesting requirements limit its year-round applicability and scalability in some regions.
## 2 Methods  
Due to the complexity of conformation searches for systems containing more than nine atoms, the conventional workflow--relying on chemical intuition or geometries from the available literature--is impractical and insufficient for the purposes of this study. The ABCluster 3.0 software [49, 50] has been successfully applied in the conformation search and global optimization of molecules and clusters [51]. Therefore, the ABCluster software was utilized to explore potential isomers of the complex systems investigated in this work. The initial guess structures of the complexes between H\({}_{2}\)SO\({}_{4}\) and HX (X = OH, Cl, Br) were constructed based on chemical intuition. For each initial conformer, the structures were optimized using the B3 LYP [52] in combination with the 6-31G(d, p) basis set. To improve the description of the weak intermolecular interactions, a dispersion correction with Becke-Johnson damping D3(BI) [53, 54] was employed. To understand how to balance accuracy and computational cost, as suggested in refs. [32, 41], the B3 LYP/AVTZ level of theory offers a sufficiently reasonable description of the H\({}_{2}\)SO\({}_{4}\)-involved complex. Consequently, the diffuse function broadened, which is a consistently polarized valence basis set called aug-cc-pVTZ(AVTZ) [55, 56], and was employed for further geometry optimization. To probe potential reaction mechanisms among these conformers, transition states (TSs) were located at the B3 LYP-D3(BI)/AVTZ level, and an intrinsic reaction coordinate (IRC) analysis was performed to identify the correct connection between each TS and the isomers. The optimized Cartesian coordinates and harmonic vibrational frequencies of conformers, including TSs for three systems, are listed in the Supplementary Materials. All DFT calculations were carried out by the Gaussian 16 program [57].  
Furthermore, for all species optimized at the B3 LYP-D3(BJ)/AVTZ level, single-point energies were computed at the CCSD(T)/AVTZ level implemented in the MOLPRO 2020 program [58, 59, 60]. Although the explicitly correlated CCSD(T)-F12b method offers higher accuracy, the results in Table S10 indicate that the conventional CCSD(T) method yields sufficiently accurate energies, differing by only 0.03 kcal/mol from those obtained with CCSD(T)-F12b. Therefore, CCSD(T) was deemed adequate and efficient for the current systems. The vibrational zero-point energies (ZPEs) calculated at the B3 LYP-D3(BJ)/AVTZ level were included in the energy comparison, unless otherwise specified.
# An In-Depth Assessment of the New BDS-3 B1C and B2a Signals
## 3 Experimental Design  
In order to evaluate the characteristics of the new signal from the BDS-3 satellites, a series of experiments was conducted in Xi'an, China; its geographic location is shown in Figure 2. The experiment used the GNSS observatory on the top of the MAC building and used a Trimble's alloy receiver (Figure 3) and a choke antenna [17]. On the same day, the same GNSS receiver was used for 24-hour continuous observations. All BDS satellites were evaluated, including BDS-2 and BDS-3 satellites. Some satellites of GPS, Galileo and QZSS were selected with the same frequency as B1C and B2a of BDS-3 for analysis. Since the sampling interval of the GNSS receiver is 1 second, 86,400 observations were collected in one day.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**GNSS/Frequency**} & **1176.45** & **1207.14** & **1268.52** & **1561.098** & **1575.42** & **2492.028** \\  & **MHz** & **MHz** & **MHz** & **MHz** & **MHz** & **MHz** \\ \hline
**BDS-2** & & B2I & B3I & B1I & & \\
**BDS-3** & B2a & B2b & B3I & B1I & B1C & Bs \\
**GPS** & L5 & & & & L1 & \\
**Galileo** & E5a & E5b & & & E1 & \\
**QZSS** & L5 & & & & L1C & \\
**NAVIC** & L5 & & & & Bs \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of BDS-3 new signals with other GNSS signals.  
Figure 2: The geographical location of Xi’an.  
In this contribution, three experiments were used to evaluate the accuracy of the GNSS signal. The first experiment evaluated the strength of the new BDS-3 signal, and the C/N\({}_{0}\) indicator was used for analysis. The second experiment analyzed the quality of code observation of the BDS-3 new signal and, CC/MP indicators were used for analysis. The third experiment used DD observations and a zero-baseline solution ([PERSON], 2012) (as shown in Figure 4) to evaluate the quality of carrier phase observations of the new BDS-3 signal.
# Biomass Fly Ash Self-Hardened Adsorbent Monoliths for Methylene Blue Removal from Aqueous Solutions
## 2 Materials and Methods
### Preparation of the Self-Hardened Monoliths  
BFA-based slurries, without and with 5 wt.% of PLC (by replacing the weight of the BFA), were prepared. They contain 0.05 wt.% (of the total solid content) of surfactant and distinct amounts of AP (0.00, 0.01, 0.05 and 0.09 wt.% of the total solids content). The use of the same surfactant concentration in all the prepared slurries was established because this type of compound interferes with the adsorption process [13]. A water to total solids weight ratio (w/s) of 0.42 was used to prepare the slurries, assuring an adequate consistency for pouring and filling the moulds. The sample notation used here based on its composition is xC:yA, xC for PLC and yA for AP percentages. For example, the 5C:9A slurry has 5 wt.% of PLC and 0.09 wt.% of AP.  
The prepared compositions are presented in Table 1.  
Single BFA or BFA/PLC blended powders were mixed in a plastic bag and then added to tap water in a mechanical mixer (KitchenAid, Artsian 175 PS, Benton Harbor, MI, USA). Mixing for 2 min at a low rotation speed (60 rpm) was conducted. The mixer was stopped for 1 min to remove the material stuck to the edges of the bowl and place it again in the centre and mixed again for 8 min at the same rotation speed (60 rpm). Then, the AP and the surfactant were added, and the mixer was turned on for another 2 min at 60 rpm. The formation of H\({}_{2}\) starts after the addition of AP to the blends and continues for some minutes. The slurries were then poured into cubical silicone moulds (2.5 \(\times\) 2.5 \(\times\) 2.5 cm\({}^{3}\)), that were previously swabed with olive oil. Afterwards, the moulds were placed in a sealed container with water at the bottom (>95% relative humidity) and cured for 7 days at ambient temperature (22 \(\pm\) 2 \({}^{\circ}\)C). Then, the moulds were placed in a laboratory oven at 50 \({}^{\circ}\)C for 5 days. The samples were removed from the mould and kept in the oven at the same temperature for 2 more days. The curing conditions were chosen following the experimental procedure described by [PERSON] et al. [5] and [PERSON] et al. [6;7].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Slurries**} & \multicolumn{5}{c}{**Raw Materials (g)**} \\ \cline{2-6}  & **Water** & **BFA** & **PLC** & **AP** & **Surfactant** \\ \hline
0C:0A & 42 & 100 & 0 & 0.00 & 0.00 \\
0C:1A & 42 & 100 & 0 & 0.01 & 0.05 \\
0C:5A & 42 & 100 & 0 & 0.05 & 0.05 \\
0C:9A & 42 & 100 & 0 & 0.09 & 0.05 \\
5C:0A & 42 & 95 & 5 & 0.00 & 0.00 \\
5C:1A & 42 & 95 & 5 & 0.01 & 0.05 \\
5C:5A & 42 & 95 & 5 & 0.05 & 0.05 \\
5C:9A & 42 & 95 & 5 & 0.09 & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of the monoliths.
## 3 Analysis of Test Results  
### Distribution Law of Rockbolt Axial Force and Interface Shear Stress  
Figure 8a shows the distribution of rockbolt axial force along the anchoring depth. It can be seen from the figure that the maximum axial force of rockbolt is 180 kN, and the minimum axial force of rockbolt is 30 kN. The axial force of rockbolt shows a decreasing trend from the dynamic load end to the other end. Figure 8b shows the distribution of shear stress at I and II interface with anchorage depth. In general, shear stress at I and II interface increases first and then decreases. Maximum value is about 18 MPa, and minimum value is about 6.2 MPa at I interface. Maximum value is about 14.3 MPa, and minimum value is about 9.2 MPa at II interface. Compared with I interface, shear stress fluctuation of II interface is relatively small.
## 4 Results and Discussion
### Results of the Ablation Study on Different Attention Mechanisms  
This section aims to compare the performance of different attention mechanisms in the same task to discuss their impact on model performance. The primary purpose of the experiment is to verify the effects of various attention mechanisms on four key indicators: precision, recall, accuracy, and F1-score. This comparison helps deepen our understanding of how attention mechanisms enhance model performance, especially in complex data processing tasks.  
The theoretical analysis of the results in Table 6 reveals significant insights. First, the standard self-attention mechanism, by computing the relationships between input features, can capture long-range dependencies. However, this mechanism may not be efficient for highly complex or noisy data, as it involves global computation of weights, which can be computationally expensive with large datasets and may obscure some crucial local features. On the other hand, the Convolutional Block Attention Module (CBAM) combines convolution operations with an attention mechanism. Not only can it capture important spatial features, but it can also enhance the model's sensitivity to specific features through channel attention, making CBAM perform better than the standard self-attention across several metrics. CBAM's fine-grained attention adjustment, particularly suitable for high-dimensional data like images, can more effectively discern which regions contain key information, thus improving overall prediction precision and recall. Lastly, the skip attention mechanism introduces skip connections, optimizing the flow of information within the network and reducing unnecessary computations, making the model more efficient and precise. This mechanism, while retaining the advantages of a deep network, avoids common issues like gradient vanishing and is particularly suitable for tasks that require large-scale feature integration and rapid response. The high precision and recall rates of the skip attention mechanism are due to its effective integration of information from various network layers, enabling the model not only to capture deep abstract features but also to attend to subtle variations in the input data, resulting in superior performance across all evaluation metrics.  
In conclusion, the ablation study on different attention mechanisms clearly demonstrates the roles and limitations of these mechanisms in enhancing model performance. The results not only affirm the effectiveness of the skip attention mechanism in improving the model's processing capabilities but also highlight the significance and application prospects of attention mechanisms within modern deep learning frameworks. These analyses enable researchers and practitioners to better select and design suitable attention mechanisms for specific tasks, maximizing model performance and efficiency.
## 2 Materials and Methods  
### Constructed Dataset and Preprocessing  
The Eastern Anatolia region of Turkey is represented by igneous rocks with different silica contents (basic, acidic, and neutral). The dataset used in this study includes samples from three subgroups of igneous rocks. These thin-section samples were classified as ultra-basic, basic, acidic, neutral, and alkaline rocks based on SiO\({}_{2}\) content and alkali saturation. Figure 1 represents the regions from which the dataset was taken and their descriptions.  
The dataset used in this study was derived from rock samples commonly found around Elazig, Turkey, categorized as igneous rocks in geology and characterized by their multi-mineral structures. The primary reason for selecting this rock type lies in its mineral diversity and variety, which were specifically chosen to enhance the algorithm's ability to identify different mineral types. In this context, the thin-section images of rocks used in the study were selected from various igneous rock types (e.g., granite, gabbro, basalt, andesite, diorite, etc.). The rock samples were cut using a rock-cutting machine in the geology laboratories of Firat University and subsequently adhered to thin-section surfaces (lam, lamella, and adhesive material) with the aid of a vacuum holder. The prepared rock samples, measuring 2 \(\times\) 3 \(\times\) 2 cm, were polished with abrasive powder (0.01 mm) in the laboratory until a smooth surface was achieved. The resulting thin sections were imaged under a polarizing microscope using both white light and cross-polarized light. Following the processes of sample cutting, polishing, and thin-section preparation, techniques such as contrast enhancement and color normalization were applied to adapt the images in  
Figure 1: Magmatic rock units using thin-section images.  
the dataset for artificial intelligence-supported algorithms. These enhancements aimed to improve the reliability and representational quality of the dataset. The dataset includes 600 cross-polarized thin-section images of different igneous rock samples prepared in the Geological Engineering laboratory of Firat University. The sizes of the images vary between 600 \(\times\) 480 and 4800 \(\times\) 3260 pixels. Quartz, alkali feldspar, and plagioclase minerals in the images were labeled and saved in PASCAL VOC format. Therefore, the dataset has superior resolving capability and sample diversity. Some samples from the dataset are given in Figure 2.  
The Python (v.3) Albumentations library was employed for image enhancement. In thin-section images taken using a microscope, measures were taken to add differences while preserving the overall structure. Table 1 illustrates the image-enhancement techniques applied to the images. In Table 1, 3654 thin-section images were subjected to comparison using random probabilities. The dataset's final iteration was derived by partitioning the images into training and validation sets in an 80-20% ratio, respectively. Figure 3 shows that although the classes are not completely equal, each class exists at a certain level.
## 2 Experimental Procedure  
### Synthesis and Characterization of the Powder  
The LSCF cathode and SDC electrolyte were prepared via glycine-nitrate and citric acid-assisted sol-gel processes, respectively. The detailed descriptions of the preparation procedures of LSCF cathode and SDC electrolyte powders are available in our previous publications [30; 31]. The LSCF-SDCC composite cathode was realized by using the following procedure: Firstly, the SDCC composite electrolyte powder was prepared by mixing the prepared SDC powder and 30 wt % binary carbonates Li\({}_{1.34}\)Na\({}_{0.66}\)CO\({}_{3}\) via high-speed ball milling technique to prepare the SDCC composite electrolyte [32]. Secondly, the prepared SDCC composite electrolyte powder was mixed with the LSCF at different weight percentages. Finally, the resulting mixture was calcined at 680 \({}^{\circ}\)C for 1 h to obtain the desired LSCF-SDCC composite cathode powders. The weight percentages of the SDCC composite electrolyte content within the LSCF cathode varied from 30 wt % to 50 wt % in order to investigate the effect of SDCC content on the electrochemical performance of the LSCF-SDCC composite cathode. The prepared LSCF-SDCC composite cathode powders were labeled as LSCF-30 SDCC, LSCF-40 SDCC, and LSCF-50 SDCC. Conversely, the anode powders were prepared by mixing nickel oxide (NiO) with 40 wt % SDCC via high-speed ball milling technique to obtain NiO-SDCC composite anodes [33].  
The X-ray diffraction (XRD) patterns of the prepared LSCF-SDCC composite cathode powders were collected by using an X-ray diffractometer (Shimadzu XRD-6000, D8-Advance, Bruker, Germany) under the following conditions: CuK\(\alpha\) (\(\lambda\) = 0.15418 nm) radiation, and 2\(\theta\) varying from 10\({}^{\circ}\) to 80\({}^{\circ}\). Energy-dispersive X-ray (EDX) spectroscopy was conducted to determine the elemental composition of the synthesized electrolyte, cathode, and anode powders, as shown in Figure 1. The EDX spectra confirmed the presence of Sm, Ce, La, Sr, Fe, Co, Ni, and Na peaks, detected from the synthesized powders. However, the Li peak cannot be detected easily by using a conventional EDX instrument, due to use of a beryllium filter. The cross-sectional microstructure of the fabricated single cells was observed through field emission scanning electron microscopy (FESEM, Merlin Compact, ZEISS, Germany). The specific surface area of the composite powders was determined using a BET surface area analyzer (Micrometrics, ASAP 2010, Norcross, Georgia USA). A laser particle size (Fritsch Analysette 22) was used to determine the particle size distribution of the prepared composite cathode powders.
# Effective Detection of Malicious Uniform Resource Locator (URLs) Using Deep-Learning Techniques
## 3 Research Methodology
### Model Training  
As shown in Figure 4, the outputs of each encoder fed into the BERT model and the Char2B model for further processing leverage contextualized and sequential dependency representations, respectively. In the transformer architecture, the encoder-only part corresponds to the BERT model, while the GPT-2 model, as detailed in [22], exemplifies the decoder-only part. BERT, being an encoder-based model, is well suited for classification tasks. It is a pre-trained on a large text corpus using the masked language-modeling objective, which enables it to generate contextualized token representations. These representations capture both semantic and contextual information, allowing the model to understand relationships within the input. In contrast, the decoder section of a transformer, as used in models like GPT-2, is designed for autoregressive tasks such as language generation, where the model predicts the next token in a sequence. However, for malicious URL detection, the goal is to classify a URL based on its features, not to generate text.  
Therefore, using the decoder would introduce unnecessary computational overhead and complexity without benefiting the task. The Char2B model is the fusion model resulting from the BERT and Char2B encoder, which is the combined effect of BERT and CharBiGRU embedding, and this means it has both word-level and char-level representation. In both models, we applied the attention layer to obtain the attention weight across the layer's head, and following this, we used the FC classier with the sigmoid activation function. Thus,the model contextualizes and captures vital information across each layer between tokens in a given sequence. This enables the model to capture intricate patterns and understand the context of the tokenized URLs, which can be essential for distinguishing between the impacts of the link.