esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Accuracy of Turbulence Model Simulations of the Exhaust Manifold
## 2 Methodology
### Turbulence Models Equations
#### 2.3.3 K-Kl-\(\omega\) Transition Model  
The k-kl-\(\omega\) transition model, an extension of the standard k-\(\omega\) model (e.g., [PERSON] [29] and [PERSON] [30]), introduces an extra transport equation for laminar kinetic energy (kl) in addition to the existing k-\(\omega\) transport equations. This augmentation is designed to anticipate the initiation and span of transition by addressing low-frequency large-scale velocity fluctuations within the boundary layer. These fluctuations serve as indicators of pre-transition processes. The model considers both natural and bypass transitions. Unlike the TSST and SSTI models, the k-\(\omega\) model relies more on physics-based principles rather than correlations.  
Within this model, there exist transport equations for turbulent kinetic energy per unit mass (\(k_{T}\)), laminar kinetic energy per unit mass (\(k_{L}\)), and the specific dissipation rate (\(\omega\)). When these differential equations are expressed in an incompressible form, they manifest as follows:  
\[\frac{Dk_{T}}{Dt}=P_{k_{T}}+R_{BP}+R_{NAT}-\omega k_{T}-D_{T}+\frac{\partial}{ \partial x_{j}}\Bigg{[}\Bigg{(}v+\frac{\alpha_{T}}{\alpha_{k}}\Bigg{)}\frac{ \partial k_{T}}{\partial x_{j}}\Bigg{]} \tag{13}\]  
\[\frac{Dk_{L}}{Dt}=P_{k_{L}}-R_{BP}-R_{NAT}-D_{L}+\frac{\partial}{ \partial x_{j}}\Big{[}v\frac{\partial k_{L}}{\partial x_{j}}\Big{]} \tag{14}\] \[\frac{D\omega}{Dt}= C_{\omega 1}\frac{\omega}{k_{T}}P_{k_{T}}+\Big{(}\frac{C_{\omega k}}{f_{W}}-1 \Big{)}\frac{\omega}{k_{T}}(R_{BP}+R_{NAT})-f_{W}^{2}C_{\omega 2}\omega^{2}\] \[+C_{\omega 3}f_{\omega}\pi f_{W}^{2}\frac{\sqrt{k_{T}}}{d^{3}}+ \frac{\partial}{\partial x_{j}}\Big{[}\Big{(}v+\frac{\alpha_{T}}{\sigma_{ \omega}}\Big{)}\frac{\partial\omega}{\partial x_{j}}\Big{]} \tag{15}\]  
where \(P\), \(R_{BP}\), \(R_{NAT}\), and \(D\) denote the turbulent production, bypass transition, natural transition, and near-wall dissipation term, respectively.
# Applications of Satellite Radar Imagery for Hazard Monitoring: Insights from Australia
## 7 Geomorphological Changes
### Example Case-Study: Geomorphological Monitoring in Remote Areas (Northern Territory)
This will be key to developing, for example, a more comprehensive understanding of fault systems and seismic hazards, which is required for nation-building infrastructure projects and expansion of population centres (e.g., [79]). SAR-derived measurements are also now beginning to contribute to compliance in Australian infrastructure projects, such as tunnelling and resource extraction, including the mining and groundwater sectors (e.g., [7]). For example, it has been recommended that the regional InSAR survey of the Gippsland Basin, Victoria, (Section 3.2.1) be repeated every 3-5 years [80]. Mining operations are required to assess potential subsidence impacts as part of Environmental Impact Statements, and the ongoing validation and updating of subsidence predictions are set to benefit from a monitoring system that can fully capture the spatio-temporal extent of subsidence, such as is the case with InSAR (Section 5). In light of the increased use of InSAR as a geodetic tool to monitor anthropogenic activities, networks of artificial corner reflectors have been installed to act as persistent high-amplitude reflections in SAR imagery and ties to Australia's growing ground-based GNSS infrastructure (e.g., Surat Basin: [60]; Perth: [15]; southwest Sydney: [81]). Combining InSAR with absolute geodetic techniques acts to validate the measurements and enable the integration of relative InSAR displacement maps into absolute reference frames defined by the GNSS sites (e.g., [82]), with proposed extensions of this approach to the Australian (and global) network of Very Long Baseline Interferometry telescopes [83].
# Water Body Mapping Using Long Time Series Sentinel-1 SAR Data in Poyang Lake
## 2 Materials and Methods
### Water Body Mapping Using WaterINet  
Since valid pre-trained U-Net cannot be migrated to SAR imagery, we modified the classic U-Net to extract water body using SAR data. The flowchart of our U-Net-based water extraction method is illustrated in Figure 7. There were two parts to this method: the encoding part (left part of Figure 7), which provides the basis for object category recognition, and the decoding part (right part in Figure 7), which provides the basis for accurate segmentation and positioning. Here, each convolution block had the same workflow, a \(3\times 3\) convolution layer with padding, a batch normalization layer, an activation layer (ReLU activator), and one more. The decoder was mainly composed of a deconvolution structure, a convolution block, and a concatenation. Finally, the deconvolution structure upsampled the feature map to achieve pixel-level classification. The gray arrows from left to right refer to the skip-connection, which can be used to fill in the low-level information to improve the segmentation accuracy. The loss function used in WaterUNet was _binary_crossentropy_, the learning algorithm was _Adam_, and the accuracy evaluation function was _accuracy_. All the algorithms are provided in the Google Tensorflow framework [65].  
The samples used to train the model were pixel-level, meaning that each pixel in the sample was labeled with an attribute value (1 for water and 0 for non-water). Due to the medium resolution of the Sentinel-1 SAR images, the backscatter coefficient threshold segmentation method and direct visual interpretation method were used to initially classify the images; then, the GIS tools were used to post-process the misclassification and fragmentation patches in the results through manual discrimination to ensure a good internal connectivity, complete boundaries, and correct types of feature categories as far as possible [43].  
Considering the resolution of the image, patches were firstly cropped to a 64 \(\times\) 64 size using a sliding window in order to better capture the spatial features of water bodies for the model (Figure 8). To better learn the features of water bodies in SAR images and considering the sample class imbalance, samples with a water body ratio of 25% to 75% were selected as sample sets. The diversity and variability of the sample data were increased through data enhancement, including data rotation by 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\). A total of 11,220 patches were obtained and used to build a more accurate water dataset. A total of 80% of the samples in the dataset were used for training and 20% were used for validation.
## 3 Applying the Methodology to the Example of Dabar Power Tunnel
### Quantitative Analysis  
#### 3.3.1 Input Data  
Whereas overbreak quantities could be determined using deterministic analysis along the entire length of the tunnel, it was not plausible from a practical point of view to perform an extreme number of complicated analyses to achieve a quantitative evaluation. As discussed before, a more comprehensive method of utilising geological mapping and considering RMR parameters was needed in order to quantify overbreaks and determine their causes. Factors that are considered to quantify overbreaks and separate the technological from geological types are (a) rock mass quality (RMR value), (b) discontinuity orientation, and (c) number of joint sets. These parameters were analysed within each mapped face of the excavation, totalling 1601 analyses.  
The RMR classification was used to evaluate tunnel sections in which the occurrence of overbreak excavation was a consequence of unfavourable geological conditions. According to the RMR classification, very favourable and favourable orientations of discontinuities relative to the tunnel excavation are scored with 0 and \(-\)2 points. Unfavourable and very unfavourable orientations are scored with \(-\)5, \(-\)10, and \(-\)12 points, depending on the severity of consequences for the excavation. These criteria were comprehensively used in order to distinguish probable and not probable wedge instability. In addition, the number of joint sets was evaluated for each section. This is a critical parameter in key block theory, as unstable wedges can form only if there are three or more sets exposed on the surface of the excavation.  
The statistical processing of all acquired data (geodetic survey, results of geological mapping, and RMR characterisation) was conducted in order to evaluate mutual dependencies, which enables distinguishing between the two overbreak types. As will be shown below, the results of the statistical analyses were used to establish a criterion for calculating the amount of technological overbreaks.
## 2 Results and Discussion  
### Volatile Constituents in the Peel and Juice  
The volatile profiles obtained from the three-finger lime samples present different VOCs profiles, as shown in Figures S1-S3, which display typical total ion chromatograms (TIC) of peel and juice of the CV Pink Pearl, Sanguinea, and Faustrime, respectively. A total of 84 volatile compounds were identified by HS-SPME/GC-MS in the peel and the juice of the three different finger lime CVs, including sesquiterpenes (33), monoterpenes hydrocarbons (19), oxygenated monoterpenes (12), aldehydes (7), esters (4), alcohols (4), and others (5), as listed in Table S1, which also reports the VOCs abbreviation code, the experimental and literature reported Kovats index, and the identification method (Table S1). Among all volatile components, 54 have been already identified in the peel extract of different finger lime CVs [1; 3; 5; 6; 7; 8; 9; 11; 12; 13], while a further 30 VOCs, which seem not been previously observed in any finger lime CV, have been reported in the volatile patterns of various _Citrus_ species [10; 14; 15; 16; 17; 18; 19]. Previous studies concerning the VOCs profiles in different finger lime CVs have highlighted that, based on a comparative qualitative and quantitative analysis of the volatile pattern in the peel extract, this fruit can be clustered into diverse chemotypes, which are not only unique in the lime species but also the genus _Citrus_[3; 5; 6; 7; 8; 9]. The comparative analysis also demonstrated the presence of volatile compounds which have never been detected in any other citrus and of some volatile constituents specific to one CV [3]. The number of volatile compounds in the finger lime peels ranged from 74 to 46 for the Pink Pearl and the Faustrime, respectively, while in the juice they ranged from 45 to 41 for the Sanguinea and the Pink Pearl, respectively (Table 1). According to the literature on _Citrus_ species, in all CVs, the number and the content of the VOCs in the peels were higher than in the juices and distinct volatile chemotypes for the three finger lime varieties of the present study were observed (Table 1) [1; 9; 16]. Figure 2A reports the volatile content overall separately detected in the pulp and the peel in all the three investigated finger lime CVs (inner annulus), while the external annulus refers to the individual contribution, in terms of the total volatile pattern that each finger lime CV has separately given to the peel and the pulp. On the other hand, Figure 2B indicates that monoterpene hydrocarbons, followed by sesquiterpenes, are clearly the most abundant VOCs identified in all fruit samples.
## 3 Result and Discussion
### Energy Analysis
Similar results were observed for \(\mathrm{SBF}_{20}\), \(\mathrm{SBF}_{40}\), \(\mathrm{SBF}_{60}\), \(\mathrm{SBF}_{80}\), and \(\mathrm{SBF}_{100}\). From Figures 8-11, \(\mathrm{SBF}40\) shows percentage increases in cooling water losses of 5.71%, 4.13%, 3.49%, and 2.67%, whereas the percentage decreases in exhaust gases loss were 4.66%, 2.01%, 1.3% and 0.8%, respectively, for 0.92 kW, 1.96 kW, 2.92 kW and 3.7 kW loading conditions compared to pure diesel, as described in Figure 8. From Figure 9, it was observed that, with an increase of biodiesel proportion in the blend, for \(\mathrm{SBF}_{60}\), 8.4%, 6.3%, 5.96 and 4.22% water-cooling losses and 7.13%, 3.22%, 2.73%, and 1.36% exhaust losses were observed for an incremental loading condition of 25%. The losses were found to be higher for a higher blend ratio [55]. As described in Figures 10 and 11, for \(\mathrm{SBF}_{80}\) the cooling water losses observed were 10.99%, 8.72%, 7.57% and 6.54%, while for \(\mathrm{SBF}_{100}\) cooling water losses were recorded as 13.76%, 11.89%, 10.68% and 8.69% respectively at 0.92 kW, 1.96 kW, 2.92 kW and 3.7 kW loading conditions. This occurs due to the high oxygen content of biodiesel, which encourages complete combustion and raises internal cylinder temperature, increasing loss via cooling water [56; 57]. The exhaust gas losses for \(\mathrm{SBF}_{80}\) were observed as 10.25%, 4.64%,3.13% and 2.34% while for \(\mathrm{SBF}_{100}\) it was recorded as 13.46%, 7.22%, 5.21%, and 3.39%, respectively.
## 5 Discussion on Engineering Application Effect
### Limitations and Suggestions  
(1) Limitations of the study  
The study determined a pillar width of 3.0 m as protective for the surrounding rock using UDEC simulation software, and this was proven to be effective. In practical engineering applications, the pillar width is influenced by factors such as geological conditions and rock mass quality. Particularly in cases of uneven coal seams or complex fault zones, a fixed width may not ensure stability, necessitating further on-site experiments and data adjustments.  
Although the method achieved favorable results in the case study, its applicability is still constrained by factors such as coal seam geological characteristics, fault size, and mining equipment. Therefore, similar results may not be achieved under comparable mining conditions or face characteristics, and the method should be adjusted based on specific site conditions.  
(2) Suggestions for future study  
More extensive validation and optimization of the adaptability of the method in different mining areas and under different geological conditions is recommended, especially in engineering contexts with large fault falls and different lithological combinations, to collect more comprehensive data for the improvement and promotion of the method.  
It is also recommended to continue to optimize the design methodology for rock pillar width under varying geological conditions. Based on numerical simulation and field test data, researches should also integrate machine learning and other advanced techniques to investigate the influence of additional variables, such as coal seam thickness, seam dip, fault displacement, and fault extension range, on the structural stability of rock pillars. This approach aims to further enhance the rationality and reliability of rock pillar design.
# Energy-Efficient Partial LDPC Decoding for NAND Flash-Based Storage Systems
## 3 Proposed Partial Decoding of LDPC Codes
### Proposed Energy-Efficient Decoding of a Partial H-Matrix  
The proposed decoding algorithm increases energy efficiency in the LDPC decoding, and is effective in reducing the energy consumption of storage systems built with NAND flash memory, since the NAND flash is reliable in the beginning stage. Applying high voltages to a cell repeatedly to program or erase the cell decreases the SNR of the flash channel monotonically [3; 4]. As the wear-leveling technique makes the SNR of a page almost the same as that of the other page [5], the NAND flash channel is reliable in a considerable amount of time. Since the NAND flash channel in the beginning does not induce many erroneous bits, the maximum-effort decoding with the full H-matrix is inefficient. Therefore, the proposed algorithm selects a proper partial H-matrix depending on the channel condition.  
Considering the channel SNR, the proposed algorithm selects a specific partial matrix from a set of partial H-matrices defined as  
\[S=\{\mathbf{H}_{2},\mathbf{H}_{3},\ldots,\mathbf{H}_{w_{c}}\}. \tag{8}\]  
The selected partial H-matrix is the initial partial H-matrix that is first used for decoding. The initial partial H-matrix for a specific SNR can be determined in advance by conducting simulations over the flash channel or by analyzing the decoding algorithm. The proposed energy-efficient decoding algorithm is described in Algorithm 2.  
```
1:Input:\(S=\{\mathbf{H}_{2},\mathbf{H}_{3},\ldots,\mathbf{H}_{w_{c}}\}\), MAI, and channel SNR
2:\(j=\) index of a partial \(\mathbf{H}\) corresponding to the channel SNR
3:Partial decoding with \(\mathbf{H}_{j}\)\(\triangleright\) from Algorithm 1
4:ifthe above decoding failsthen
5:if\(j<w_{c}\)then
6: set\(j=j+1\) goto step 3:\(\triangleright\) Extend H
7:else
8:report a decoding failure
9:endif
10:else
11:report a decoding success\(\triangleright\) Early termination
12:endif
```  
**Algorithm 2** Energy-efficient LDPC decoding.  
Figure 1: Uncorrected BER performances of various partial H-matrices obtained from a (149, 61, 6) array LDPC code.
## 2 System Model and Connection Setup
### Channel Model, Estimation and Statistics Evaluation
Furthermore, \(\beta_{d}(t)\) considers the shadowing effect of sea stream, dives, and fish schooling, according to [44], while \(\
u_{d}=v\cos(\phi_{d})f_{c}/v_{c}\) takes care of the Doppler effect through the direction \(\phi_{d}\), with \(v\) being the node speed, \(v_{c}\) the speed of sound and \(f_{c}\) the reference frequency. For some time intervals and propagation scenarios, the channel can be considered time-invariant. As outlined at the beginning of this section, we assumed \(T_{f}\leq T_{coh}\) to reasonably meet such conditions. Hence, we have \(\rho_{p}(t)=\rho\) within a frame, and the received analog signal obtained from the transmission of a pilot can be represented as:  
\[r_{e}(t)=\sum_{d=1}^{\rho}\beta_{i}d(t)a_{d}x(t-\tau_{d})e^{j2\pi\
u_{d}t}+z(t) \tag{9}\]  
that represents an exhaustive model, since it includes several propagation effects, from multipath to shadowing, due to obstacles or fish schooling.
# Green Macrolgae Biomass Upcycling as a Sustainable Resource for Value-Added Applications
## 3 Results and Discussion
### Phenolic Compounds  
Phenolic acids and flavonoids, though less studied in green algae compared to other phyla, have been identified in various forms such as rutin, quercetin, and catechin derivatives. The production and diversity of these compounds are influenced by stress factors, which enhance defensive responses and compound complexity [69; 70].  
In this study, significant variation in phenolic and flavonoid content was observed across different solvent extracts (Table 3), reflecting different affinities of solvents based on the polarity and solubility of extracted compounds. Due to its high polarity and hydrogen-bonding capacity, water demonstrated higher efficiency in extracting total phenolics [71], while methanol showed better performance on flavonoid extraction. This finding suggests the presence of non-flavonoid phenolics in aqueous extracts and the potential for flavonoid enrichment using algae extracts obtained with alcoholic solvents [72].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Samples** & **TPC (mg GAE/100 g)** & **TFC (mg QE/100 g)** \\ \hline WAT & 41.50 \(\pm\) 0.06 b  & 1.77 \(\pm\) 0.002 a  \\ ETA & 6.14 \(\pm\) 0.03 a  & 31.50 \(\pm\) 0.03 b  \\ MET & 7.83 \(\pm\) 0.02 a  & 59.33 \(\pm\) 0.10 c  \\ \hline \hline \end{tabular}
\end{table}
Table 3: Total phenolic (TPC) and total flavonoid (TFC) contents in aqueous (WAT), ethanolic (ETA), and methanolic extracts (MET) derived from oven-dried green algae _Ulva_ sp. (UODP).  
Figure 4: Detected pigments (chlorophylls and carotenoids) in oven-dried green algae _Ulva_ sp. (UODP).  
A TPC of 55.61 mg GAE/100 g was reported for methanolic extracts of _Ulva lactuca_[63]. Similarly, higher results have been shown in the literature, such as by [PERSON] et al. [73] who found approximately 90 mg GAE/100 g using a methanolic extraction for _Ulva_ species and by [27] who reported 74 mg GAE/100 g in _Ulva rigida_.  
For TFC, methanol was the most effective extractor among the investigated solvents (\(p<0.05\)). This is consistent with [PERSON] et al. and [PERSON] et al. [1,74], who highlighted methanol's capacity to extract flavonoids of varying polarities. Likewise, [PERSON] et al. [75] studied the green seaweed _Chaetomorpha_ sp. and reported lower TFC (approximately 20 mg QE/100 g, fresh weight) in ethanolic extracts.  
The variations in TPC and TFC emphasize the influence of solvent polarity, extraction conditions, and matrix effects, and their correlation suggests that phenolic compounds other than flavonoids are present in the aqueous extracts. Additionally, these differences can be attributed to biotic and abiotic factors, such as species-specific and environmental influences on phytochemical profiles [70]. This study demonstrates the importance of selecting appropriate solvents to optimize the extraction of specific bioactive compounds of interest.
## 2 Nanofluid Thermal Conductivity
### Effects of Hybrid Nanoparticles on Nanofluid Thermal Conductivity
For a given temperature, the highest thermal conductivity was observed for the nanoparticle mixture ratio of 20:80, while the 50:50 nanofluid had the lowest thermal conductivity. The results showed an increase in nanofluid thermal conductivity with the increased amount of SiO\({}_{2}\) nanoparticles in the mixture, with the exception of the 50:50 nanofluid. It was suggested that this was likely a result of the smaller size of the SiO\({}_{2}\) nanoparticles (22 nm) in comparison to the TiO\({}_{2}\) nanoparticles (50 nm) allowing the SiO\({}_{2}\) nanoparticles to fill in the spaces between the TiO\({}_{2}\) nanoparticles, thus leading to increased contact area for conduction between molecules, and therefore an increased rate of heat transfer as a result of Brownian motion collisions. [PERSON] and [PERSON] [68] developed new experimental correlation for the thermal conductivity of Ethylene Glycol containing Al\({}_{2}\)O\({}_{3}\)-Cu hybrid nanoparticles. Additionally, [PERSON] et al. [14] measured the thermal conductivity of nanofluids composed of nanodiamond and Co\({}_{3}\)O\({}_{4}\) nanoparticles dispersed in water and water- Ethylene Glycol mixtures of various concentrations. A linear increase in thermal conductivity with temperature and concentration was observed for each combination. However, higher thermal conductivity enhancement was observed for the water-based nanofluid in comparison to the nanofluids containing Ethylene Glycol. Through examining the experimental findings discussed above, it can be suggested that the thermal conductivity of hybrid nanofluids tends to increase with temperature and nanoparticle concentration, similarly to nanofluids containing a single type of nanoparticle, likely as a result of Brownian motion and an increased area for heat transfer at higher nanoparticle concentrations. However, further experimentation should be completed in order to determine how well various materials of nanoparticles interact with each other and with the base liquid in which they are dispersed. Additionally, further investigation should be completed in order to develop a better understanding of the exact mechanisms which lead to enhanced thermal conductivity in hybrid nanofluids.