esa-sceva/satcom-chunk-collection huggingface dataset:
# Analyzing the Long-Term Phenological Trends of Salt Marsh Ecosystem across Coastal LOUISIANA
## 3 Results
### Mann-Kendall Trend Analysis  
Trend classes for changes in the base values, max values, seasonal amplitudes, and small seasonal integrals (refer to Table 1 for description and phenological significance) for the southeast LA salt marshes are shown in Figure 7. We observed positive changes in the GBM base values in almost 75.5% of the salt marsh area in southeast LA, with strong positive changes observed in 31.6% of the area. However, the max values did not show as much positive change; we observed strong positive changes in only 7.2% of the total salt marsh area. On the other hand, we observed mostly negative changes in the seasonal amplitude and small seasonal integral, two parameters that are indicators of net seasonal photosynthesis rates and biomass productions. Most (70.3%) of the salt marsh ecosystem showed negative changes in seasonal amplitude (with 12.1% of the area showing a strong negative change), while 67.9% showed negative changes in the small seasonal integral (with 10.9% of the area showing a strong negative change). Overall, the areas with positive changes in base values and negative changes in the seasonal amplitude and small seasonal integral matched well (Figure 8).  
Figure 7: Percentage of area coverage with different trends for (**a**) GBM base value; (**b**) GBM max value; (**c**) GBM amplitude; and (**d**) GBM small seasonal integral for the salt marshes in Louisiana.
# An Integration Model for Flux Density Distribution Formed by a Heliostat
## 2 Methodology
### Flux Density Computation Method  
When considering the intensity of the reflected solar ray, we first divided the heliostat into smaller grids, and the center point of each grid was used to represent the grids. Then, we solved the flux density contribution of all reflected rays to a certain point on the receiver plane. Finally, we integrated these contributions to compute the flux density value of a specific point on the receiver plane [19].  
The actual calculation process of a specific point is mainly divided into four steps:  
1. Location determination.
2. Meshing process and coordinate system rotation.
3. Calculation of geometrical optics.
4. Solution of the flux density at a specific point.  
The calculation details are shown in Figure 4. Further, the flux density of other grid points was calculated according to the above method. As shown in Appendix A, the actual calculation process is very complicated, so we developed a numerical code to help us complete the analysis and calculation.  
To verify the reliability of the proposed method, we calculated the root mean square error (RMSE) of the flux density and intercept factor between the models and experimental data [19]:  
\[RMSE=\sqrt{\frac{\sum_{l}^{u}\sum_{l}^{v}[F_{1}(u,v)-F_{2}(u,v)]^{2}}{uv-1}} \tag{28}\]  
where \(F_{1}(u,v)\) and \(F_{2}(u,v)\) are the flux density calculated by the model and measured data, respectively. This formula can also calculate the root mean square error between two different models.
# A Brief Review on Challenges in Design and Development of Nanorobots for Medical Applications
## 2 Challenges in the Design and Development of Nanorobots
### Powering and Navigation
#### 2.3.2 Fuel-Free Discussion  
The field of fuel-based motors is significantly advanced; however, some issues regarding the concentrations of used fuels, biocompatibility, in vivo toxicity, and the capacity for oxidation create an obstacle for their application, particularly in healthcare. Due to decreased fuel levels and the requirements in special cases, fuel-driven motors were replaced by fuel-free motors. Additionally, in order to overcome fuel dependency problems, fuel-free motors provide an improved trajectory and enhanced rotation and orientation. Fuel-free propelled motors act as driving sources by using the energy provided by external sources such as electric and magnetic fields, light, and ultrasound. Fuel-free propulsion provides advantages such as on-demand motion control, biocompatibility, lifespan, versatility, and sustainability by involving biocompatible external stimuli.
# Data Privacy and Ethical Considerations in Database Management
## 4 Case Study
### Ethical Considerations in Using Synthetic Healthcare Data  
While using synthetic data addresses some privacy concerns, it is important to consider the ethical implications:  
* Representation and Bias: Synthetic data may not accurately represent the complexities and nuances of real patient data, potentially introducing or amplifying biases [66].
* Validity of Research: Results obtained from synthetic data may not be as reliable or generalizable as those from real data, raising ethical questions about the validity of any conclusions drawn [67].
* Transparency: It is crucial to be transparent about the use of synthetic data in any analysis or research to maintain scientific integrity [68].
* Data Quality: The quality of synthetic data depends on the algorithms and methods used to generate it. Ensuring high-quality synthetic data is an ethical responsibility to prevent misleading results [69].
# Getting Ready for Large-Scale Proteomics in Crop Plants
## 1 Introduction
The number of published comprehensive crop proteomes with relevance to human nutrition is still very small. The wheat (_Triticum aestivum_) proteome has been mapped to a depth of almost 16,000 proteins [27], that of maize to nearly 18,000 proteins [28] and there is a multitude of smaller-scale reports often on specific tissues, notably fruits. Tomato (_Solanum Lycopersicum_) is an interesting case as it is one of the most important crops worldwide with a global production nearing 200 million tons per year [29]. Tomato fruit is among the more challenging crop plant tissues to work with due to the presence of high amounts of sugars and secondary metabolites. Initial attempts to analyze the tomato fruit proteome led to the identification of a rather modest number of protein groups (85-1140, summarized in [30; 31]). A substantial step forward was made by [PERSON] et al., who reported the identification of 5404 proteins in seedless fruits following a multitude of sample preparation optimization steps and MS parameter adjustments [32]. Less than one year later, [PERSON] et al. characterized the proteomes of tomato fruit skin and flesh at five time points during fruit development and reported the identification of 7738 proteins using a combination of FASP for protein digestion and high-pH reversed-phase liquid chromatography for peptide fractionation [30]. The authors of the present work have recently launched an initiative to map the proteomes of the 100 most important crop plants for human nutrition. This basic science project asks questions such as (i) which of the genes of a crop plant produce a protein product, (ii) where these proteins are expressed in the plant and (iii) in which approximate quantities. In order to realize this ambitious goal, it is necessary to reliably extract, prepare and analyze the proteins from all these different crops with their respective characteristics. In the current manuscript, we report on the development of an end-to-end workflow (from sample preparation to LC-MS/MS measurement, to protein identification and quantification) which enables us realize this project. All the elements have been published before, but combining them in the way described enables high-throughput, high quality and high proteomic coverage at the same time. The merits of the workflow are exemplified by the analysis of whole tomato fruits leading to the identification of 140,000 peptides and 9900 proteins with a median quantitative precision of <10% coefficient of variation (CV).
# Multi-Classifier Feature Fusion-Based Road Detection for Connected Autonomous Vehicles
## 4 Proposed TLFC-RD Method
### Two-Level Fusion of Classifiers for Road Detection
#### 4.5.5 Svm  
In general, SVM [28] depends on the statistical learning theory and is used to perform road detection by using the \(FFM\). The optimization issue of SVM is converted into a convex problem by using the radial basis kernel function. This is used to avoid the local minimum and obtains the classification of global optimization during the road detection of CAVs. Equation (21) shows the kernel function of the SVM:  
\[K\big{(}FFM_{i},\ FFM_{j}\big{)}=e^{-\|\frac{(FFM_{i}-FFM_{j})^{2}}{2r^{2}}\|} \tag{21}\]  
where \(r\) is the radius, and \(K\big{(}FFM_{i},\ FFM_{j}\big{)}\) is additionally used in the next-level classification to detect the road. Here, the SVM is also used as a second-level classifier to predict a road by using the feature maps from the first-level classifiers. Figure 4 shows the architecture of the two-level fusion of the classifiers  
Figure 4: Architecture of TLFC.  
Hence, the TLFC allows the better detection of roads with the KITTI and CamVid datasets. The performance of the TLFC-RD is mainly improved by the following four strategies: (i) the cross fold process at input and pre-processing using superpixel generation, which is used to minimize the complexity during the classification; (ii) an optimal feature extraction from the images is used to provide precise classification; (iii) multi-classifier feature fusion; and (iv) the TLFC-based classification of images provides better classification based on the generated feature maps.
# Suppressing Salt Transport through Composite Pervaporation Membranes for Brine Desalination
###### Abstract  
Pervaporation membranes have gained renewed interest in challenging feedwaters desalination, such as reverse osmosis (RO) concentrated brine wastewater. In this study, composite polyvinyl alcohol (PVA)/polyvinylidene fluoride (PVDF) pervaporation membranes were prepared for brine treatment. The composite membrane was firstly studied by adjusting the cross-linking density of PVA by glutaraldehyde: the membrane with higher cross-linking density exhibited much higher salt rejection efficiency for long-term operation. A trace of salt on the permeate side was found to diffuse through the membrane in the form of hydrated ions, following solution-diffusion mechanism. To further suppress the salt transport and achieve long-term stable operation, graphene oxide (GO) was incorporated into the PVA layer: the addition of GO had minor effects on water permeation but significantly suppressed the salt passage, compared to the pure PVA/PVDF membranes. In terms of brine wastewater containing organic/inorganic foulant, improved anti-fouling performance was also observed with GO-containing membranes. Furthermore, the highest flux of 28 L/m2h was obtained for the membrane with 0.1 wt. % of GO using 100 g/L NaCl as the feed at 65 \({}^{\circ}\)C by optimising the pervaporation rig, with permeate conductivity below 1.2 uS/cm over 24 h (equivalent to a salt rejection of \(>\)99.99%).  
Keywords:brine wastewater treatment; pervaporation; composite PVA/PVDF membrane; graphene oxide; anti-fouling properties +
Footnote †: journal: Applied Sciences
# Application of Foundry Wastes in Eco-Efficient Construction Materials: A Review
## 5 Valorization of Foundry Waste in Construction Materials  
Efforts to develop eco-efficient materials using foundry waste are essential to achieve more sustainable and ecological production practices in the foundry and construction industries. The incorporation of foundry wastes makes it possible to reduce landfill accumulation, conserve natural resources, and enhance the performance of construction materials.  
Concrete has a significant environmental impact due to raw material extraction [17], but using industrial waste as a partial substitute for natural aggregates helps address this issue [18]. Among these residues, foundry sand and slag have proven to be promising byproducts for improving concrete performance. The incorporation of industrial byproducts, such as foundry waste, can lead to improvements in energy efficiency and environmental sustainability within the industry [20].  
In the following section, the potential applications of various types of foundry waste in the development of eco-efficient construction materials will be explored. The objective of this section is to highlight the diversity of construction materials that have been studied in recent years, with the incorporation of different types of foundry waste. The waste materials presented originate from different industries and possess unique compositions, contributing to the diverse behavior of the construction materials studied. The construction materials have been categorized according to the type of waste incorporated, namely: waste foundry sand, foundry slag, electric arc furnace dust, ceramic mold shells and paraffin waves waste, and foundry waste mixtures.  
In addition, an analysis of the replacement content of different residues in construction materials is provided in the following subsections. Figure 10 summarizes the findings. Waste foundry sand and ceramic mold shells are capable of replacing aggregates in construction materials, while slags and electric arc furnace dust can serve as replacement for both aggregates and cement. Most residues exhibit a broad content range, except for the electric arc furnace dust used as cement replacement, which shows the narrowest and lowest content percentage.
# Building Reliable Massive Capacity SSDs through a Flash Aware RAID-Like Protection +
## 4 Design of mSAPL
### Partial-Stripe Parity Space of mSAPL  
We introduce a method for reducing parity management overhead through Partial-stripe Parity (PP). If the user request does not arrive while filling the stripe or if the flush command comes in, the stripe may not be completely filled. That is, the data that are not protected by the parity become unprotected for a certain period of time in the stripe. In order to protect such data, PP is written in the current write stripe. mSAPL provides a way to minimize this parity management overhead.  
Figure 4 shows how mSAPL performs parity writing. It is assumed that the SSD has six flash memory chips and it has two blocks per chip and four pages per block. One stripe is composed of pages that correspond to the same page offset of each chip. In the figure, stripe 0 consists of user data LBN D0-D3 and full stripe parities FPp and FPq. Subsequently, D4 to D7 are written to the 2 nd pages of the block 0 across all the chips and stripe 1 is constructed with two full stripe parities (FPp and FPq). The remaining D8 and D9 of the write request are written to the third pages of block 0 on the chip 0 and 1, respectively. After a certain period of time (in our evaluation, 50 ms is specified), the PPs are written PP block (block 1) in order to protect the partial stripe. mSAPL maintains logical to physical (L2P) and PP map table on SDRAM. In the map, LPN, CN, PBN, and PPN denote the logical page number, chip number, physical block number, and physical page number, respectively. The map contents is periodically flushed to flash memory in order to keep consistency of the map tables [59].  
By doing this, the cost of copying valid pages during GC is greatly reduced, since adjacent PPs, which belong to the same block, are invalidated at a similar period. If the PPs in one block, which are erase units, are invalidated at the same time, then the GC cost decreases [57,60].  
Figure 4: Example of writing partial stripe parity of mSAPL.
## 4 Experimental Verification of the Master S-N Curve  
Figure 9 presents the static-load experimental device [37]. The loading force generated by the jack was equally distributed by the distribution beam on the deck plate of the specimen. Therefore, the bending moment of the specimen was consistent between two loading points. The resistance-type strain gauges with the length of 10 mm and the width of 2 mm were adopted as strain-measurement sensors. Due to the symmetry of the structural geometry shape and load, Figure 10 shows the strain gauges distribution of half of the specimen. In this figure, the uniaxial strain gauge was adopted with sensitive grid size of 10 mm \(\times\) 3 mm, a resistance value of 120 \(\Omega\) and a sensitivity coefficient of 2.06. Fourteen strain gauges were distributed on the bottom of the deck, as shown in Figure 10.  
Figure 8: Error comparison of different approaches.  
Figure 9: Static-load experimental device.  
Figure 11 shows a comparison between the numerical simulation results and experiment results of the 3U deck-rib component. The experimental study of the deck-rib welding details was undertaken by [PERSON] et al. [37], as shown in Figures 9 and 10. The geometric dimensions, boundary conditions and loading forces of FEM were the same with that of the experiment, as shown in Figure 4. The deck and rib thickness were 16 mm and 8 mm, respectively. The simple support constraints were applied to the left and right ends of the model, and the static-load concentrated force F was 18 KN. In Figure 11, the path of the deck near the weld side at the cross-section Z = 150 mm was selected. The horizontal coordinate X is shown in Figure 4, and the vertical coordinate is the transverse stress \(S_{x}\). In the figure, the curve and the dots represent the numerical simulation results and the experimental results, respectively. The experimental results are the transverse stresses measured by the strain gauges in Figure 10. Figure 11 shows that the maximum value of the numerical simulation results is approximately 294 MPa at the weld toe. The second largest value is approximately 254 MPa at the weld root. The minimum value is approximately 54 MPa. This value was obtained between the weld toe and root (6 mm from the weld root). The experimental results are in good agreement with the numerical simulation results, except for the stress at the weld toe and root. Since the stresses at the weld toe and root cannot be measured in this experiment, the numerical simulation results are considered reliable.  
Figure 12 presents the strain gauges distribution in the fatigue experiments [36]. The strain gauges are used to measure nominal stress and located in the middle of two adjacent welds, as show in Figure 12. Table 2 shows the experiment results of the 3U deck-rib component [37]. The fatigue cracks were all located at the weld toe, and most of them were on the welds of WJ-2 and WJ-5, which are  
Figure 11: Comparison of the numerical simulation results with the experiment results.  
Figure 10: Strain gauges distribution (units: mm).
## 3 Results and Discussion
### Viscoelastic Behavior of CPC-Stabilized Emulsions  
In comparison to steady-state flow measurements, the oscillatory (dynamic) measurements can be used to obtain information on the viscoelastic and gel characteristics of protein-stabilized emulsions as it depends on protein denaturation, aggregation, and their conformational changes in the system [45; 46].  
The thermal gelation process of globular proteins typically involves two main steps: (1) heat-induced unfolding of the native protein structure by exposing the interaction sites and (2) intermolecular aggregation of unfolded proteins into a spatial network [33; 57]. The physical gelation behavior of protein dispersions can be studied _via_ rheology, where the bulk viscoelastic properties can be used to examine the transition from a liquid state to a solid-like gel. Overall, the thermal gelation of globular proteins in the emulsion is a complex physicochemical process driven by various inter- and intramolecular interactions. We assumed that, using rheological data, conformational changes during the thermal gelation process of proteins can be directly correlated with the increases in bulk viscoelastic properties, both for the CPC-stabilized oil-in-water emulsion and for the CPC water dispersion. The viscoelasticity of the tested systems was analyzed by studying the storage (G\({}^{\prime}\)) and loss modulus (G\({}^{\prime\prime}\)) at a constant oscillation frequency and strain amplitude during heating from 20 to 95 \({}^{\circ}\)C, holding at 95 \({}^{\circ}\)C, and then cooling to 20 \({}^{\circ}\)C. Before heating, the emulsions and their water dispersions were held at 20 \({}^{\circ}\)C.
# A Review of Methods and Applications for a Heart Rate Variability Analysis
## 7 Applications of HRV Analysis
### Cardiac Arrhythmia Detection
The examination of HRV in patients with AF has the potential to provide significant insights into the role of the autonomic nervous system in the development of the arrhythmia, as well as its potential for predicting unfavorable outcomes or informing treatment strategies. However, it is imperative to use caution when interpreting the findings and to consider them in connection with other clinical and diagnostic factors. Relying just on an HRV analysis may not yield a thorough understanding of a patient's cardiac well-being. A study by [PERSON] et al. (2018) ranked HRV features for atrial fibrillation detection [139]. They found that the PNN50, SD1/SD2, and RR mean are the three top ranked HRV features, as well as the full set of HRV features achieving a 13%-point higher arrhythmia detection performance compared to the set of beat morphology features. Other recent studies for arrhythmia detection with an HRV analysis can be found in [140; 141; 142; 143]. The experimental results revealed that the HRV descriptors are effective measures for AF identification. In another study, [PERSON] et al. (2019) proposed an arrhythmia detection technique using a time-frequency (T-F) analysis of HRV features [144]. The back-propagation neural networks, in combination with three different types of decision rules, were employed to achieve the classification accuracy of 95.98% for the low-frequency (LF) band and 97.13% for the high-frequency (HF) band [144]. A study by [PERSON] et al. (2022) highlighted the development of a client-server paradigm for analyzing HRV features to detect arrhythmias [145]. The proposed platform performed all the real-time steps, from feature extraction to classification. Smoothed Pseudo Wigner-Ville Distribution (SPWVD) was used for the HRV analysis, and SVM was used as the classifier to achieve a classification accuracy up to 97.82%. The classification result can be further sent to clinicians for an interpretation and diagnosis.