esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion  
### Experimental Results  
Figure 10 shows the temperature distribution of the casing and end covers obtained from the CFD simulation calculations. The heat generated by the input bearing was high, therefore the end covers near the input bearing appeared as local high-temperature areas. The temperature gradually decreased from the top to the bottom of the casing. In the temperature experiment, the wall temperatures of the casing and the end covers of the intermediate gearbox were recorded using the temperature indicating plate. The 1-12 numbers in Figure 10 represent the measured points at the same position as the experimental temperature measured points (Figure 4). Figure 11 compares the CFD and  
Figure 9: Flow chart of the coupled thermal-fluid approach. (MRF model refers to multiple reference frames (MRF) model, and VOF model refers to volume of fluid (VOF) multiphase model.)  
experimental data. The maximum relative error was 10.636%, which occurred at measuring point 2, and the minimum relative error was 0.837%, which occurred at measuring point 12.  
The oil temperature in the oil pool recorded during the experiment was 96.1 \({}^{\circ}\)C. The oil temperature at the corresponding position calculated by the FLUENT software was 99.156 \({}^{\circ}\)C. This represented a relative error of only 3.18%. Furthermore, the oil temperature satisfied the limit requirement (<110 \({}^{\circ}\)C) and the content of oil met the engineering application standard. These comparisons between the CFD and experimental data showed that the CFD simulation can well predict the temperature distribution of the intermediate gearbox with splash lubrication.
###### Abstract  
Net Primary Productivity (NPP) is a basis of material and energy flows in terrestrial ecosystems, and it is also an important component in the research on carbon cycle and carbon budget. This paper evaluated the spatial distribution pattern and temporal change trends for forest NPP simulated by the LPJ (Lund-Potsdam-Jena) model and NDVI (normalized difference vegetation index) in the Yangtze River basin from 1982 to 2013. The results revealed that: (1) the spatial distribution of the forest NPP and NDVI in the Yangtze River basin has gradually decreased from the southeast coast to the northwest. The forest NPP and NDVI in the mid-lower Yangtze were higher than that of the upper Yangtze; (2) the forest NPP and NDVI in most areas of the Yangtze River basin were positively correlated with the temperature and precipitation. Moreover, the correlations among the temperature with the forest NPP and NDVI were stronger than that of correlations among precipitation with forest NPP and NDVI. Moreover, the extreme drought event in the year of 2004-2005 led the NPP to decrease in the middle and lower Yangtze River basin; (3) human activity such as major ecological projects would have a certain impact on the NPP and NDVI. The increase in forest areas from 2000 to 2010 was larger than that from 1990 to 2000. Moreover, the increasing rate for the NDVI was higher than that of NPP, especially after the year 2000, which indicates that the major ecological projects might have great impacts on the vegetation dynamics. Moreover, more attention should be paid on the joint impacts of human activity and climate change on terrestrial NPP and NDVI.  
Keywords:forests; NPP and NDVI; Yangtze River basin; climate change; human activity +
Footnote †: journal: Remote Sens. 2019, 11, 1451; doi:10.3390/rs11121451
## 3 Results and Discussion
### pH Values
The presence of an additional LA-5 strain (in the K samples) may cause competition for the substrate (an antagonistic relationship) and result in slowing down the metabolic activity of other _Lactobacillus_ sp., responsible for the acidification of the environment, and, thus, the higher pH of the K samples compared to the OK samples and the higher survival of the BB-12 bacteria in the K samples compared to the OK samples. These results evidently show that interactions of individual strains may give unexpected results. The coexistence of various bacterial strains in a fermented beverage environment can lead to competition for the available substrates, such as lactose. LA-5, known for its high metabolic activity, may consume lactose more rapidly in samples A, where there is no competition from other lactic acid bacteria. Consequently, the faster consumption of substrates by LA-5 in samples A can lead to a more rapid acidification of the environment and a decrease in pH, negatively affecting the survival of LA-5 itself. In contrast, in samples K, the presence of other bacterial strains present in the original kumis may limit the availability of lactose for LA-5, thus slowing down the acidification process. As a result, the pH in samples K decreases more slowly, which may contribute to the better survival of LA-5 compared to samples A. [PERSON] et al. [73] emphasized that the pH of fermented beverages may vary depending on the probiotic microflora used during production. This finding is corroborated by the observations in our study, where laboratory samples fermented with _Lactobacillus acidophilus_ or _Bifidobacterium animalis_ subsp. _lactis_ exhibited different pH values. Additionally, our results indicate that the storage time of laboratory-fermented beverages made from mare's milk significantly impacted pH reduction in all products. Similar pH changes were reported by [PERSON] et al. [46] in beverages fermented from goat's milk using probiotic cultures. These authors observed a pH decrease after three weeks of storage compared to day 1. A similar pattern was noted in goat's milk fermented with probiotic cultures [46]. The pH fluctuations observed during the cold storage of fermented milk can be attributed to the ongoing fermentation activity of the monocultures used in production. Although at 5 \({}^{\circ}\)C this enzymatic breakdown of lactose into lactic acid proceeds at a much slower rate than at temperatures optimal for thermophilic lactic acid bacteria, it still contributes to the gradual pH reduction. The literature data concerning cow's milk [61, 74] also confirm the pH changes observed in unfermented beverages.
# Retinal OCT Images: Graph-Based Layer Segmentation and Clinical Validation +
###### Abstract  
Spectral-domain Optical Coherence Tomography (SD-OCT) is a critical tool in ophthalmology, providing high-resolution cross-sectional images of the retina. Accurate segmentation of sub-retinal layers is essential for diagnosing and monitoring retinal diseases. While manual segmentation by clinicians is the gold standard, it is subjective, time-intensive, and impractical for large-scale use. This study introduces an automated segmentation algorithm based on graph theory, utilizing a shortest-path graph-search technique to delineate seven intra-retinal boundaries. The algorithm incorporates a region of interest (ROI) selection to enhance efficiency, achieving a mean computation time of 0.93 s on standard systems suitable for real-time clinical applications. Image denoising was evaluated using Gaussian and wavelet-based filters. While wavelet-based denoising improved accuracy to some extent, its increased computation time (~10 s/image) was the trade-off. The intra-retinal layer thicknesses computed by the segmentation algorithm was consistent with previous studies and demonstrated high accuracy with respect to manual segmentation, thus indicating clinical relevance. Future research will explore integrating machine learning to improve robustness across diverse retinal pathologies, enhancing the algorithm's applicability in clinical settings.  
retinal OCT image processing; intra-retinal layer segmentation; denoising; graph-search; image filtering; ophthalmology +
Footnote †: _Applied Sciences_  
[PERSON]  
1 Department of Biomedical Engineering, University of Illinois Chicago, Chicago, IL 60607, USA 12 School of Optometry and Vision Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada 23 Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada 3  
Footnote 1: [[https://doi.org/10.3390/app15168783](https://doi.org/10.3390/app15168783)]([https://doi.org/10.3390/app15168783](https://doi.org/10.3390/app15168783))
## 4 Method for Calculating Rebar Mesh Spacing
### Detection of Rebar Mesh Intersections  
The intersections of the rebar mesh are key structural features, and accurately locating these intersections is crucial for the subsequent measurements of reinforcement spacing. To address this, a filtering-based method for intersection detection is proposed in this paper. This method utilizes a circular-like filter kernel for pixel-level processing, ensuring the effective extraction of intersection locations. Unlike traditional methods, this approach leverages the local response characteristics of the filter kernel, fully utilizing the prominent features of intersection pixels to accurately detect the points where vertical and horizontal rebars intersect.
###### Abstract  
Traffic prediction is a vitally important keystone of an intelligent transportation system (ITS). It aims to improve travel route selection, reduce overall carbon emissions, mitigate congestion, and enhance safety. However, efficiently modelling traffic flow is challenging due to its dynamic and non-linear behaviour. With the availability of a vast number of data samples, deep neural network-based models are best suited to solve these challenges. However, conventional network-based models lack robustness and accuracy because of their incapability to capture traffic's spatial and temporal correlations. Besides, they usually require data from adjacent roads to achieve accurate predictions. Hence, this article presents a one-dimensional (1D) convolution neural network (CNN) and long short-term memory (LSTM)-based traffic state prediction model, which was evaluated using the Zenodo and PeMS datasets. The model used three stacked layers of 1D CNN, and LSTM with a logarithmic hyperbolic cosine loss function. The 1D CNN layers extract the features from the data, and the goodness of the LSTM is used to remember the past events to leverage them for the learnt features for traffic state prediction. A comparative performance analysis of the proposed model against support vector regression, standard LSTM, gated recurrent units (GRUs), and CNN and GRU-based models under the same conditions is also presented. The results demonstrate very encouraging performance of the proposed model, improving the mean absolute error, root mean squared error, mean percentage absolute error, and coefficient of determination scores by a mean of 16.97%, 52.1%, 54.15%, and 7.87%, respectively, relative to the baselines under comparison.  
support vector regression; long short-term memory; gated recurrent units; one-dimensional convolution neural network; Zenodo and PeMS datasets +
Footnote †: journal: Applied  
Acticle
## 3 Microalgal Mechanisms for Wastewater Bioremediation
### Co\({}_{2}\) Fixation  
The carbon concentrating mechanism (CCM) and photosynthetic carbon metabolic pathways are the mechanisms that microalgae utilize for CO\({}_{2}\) fixation when they are grown in wastewater [142]. Microalgae may absorb photons from the sun and use CO\({}_{2}\) or \(HCO_{3}^{-}\) dissolved in wastewater as a carbon source for photosynthesis, CO\({}_{2}\) can enter the cell through diffusion, and \(HCO_{3}^{-}\) enters the cell through active transport [5,142]. Furthermore, they use heterotrophic or mixotrophic modes to absorb inorganic and organic carbon from wastewater for metabolic control and the production of biomolecules such as carbohydrates and lipids [5,143,144]. The biological reaction of carbon fixation and biomolecule production is shown in Equations (1) and (2) [5].  
\[\begin{split} HCO_{3}^{-}+H^{+}\leftarrow(&\mathit{ Carbonic~{}anhydrase1})\to CO_{2}+H_{2}O-(\mathit{Light~{}energy})\to Rubisco\\ &\to Glyceraldehyde3~{}phosphate\to\mathit{Carbohydrates}\end{split} \tag{1}\]  
\[\begin{split} Glucose-(\mathit{Glycolysis})\to Pyruvate\to Acetyl CoA\to Malonyl CoA\to FFAs\ (\mathit{free~{}fatty~{}acids})\to TAGs\end{split} \tag{2}\]  
where Equation (1): CO\({}_{2}\) fixation and carbohydrate generation; Equation (2): glucose utilization to pyruvate, acetyl-CoA, and fatty acid synthesis.
# Connected Vehicles: Technology Review, State of the Art, Challenges and Opportunities
## 2 Enabling Technologies  
The increasing demand for an \"always connected\" paradigm is pushing automakers to find more innovative software and electronics design that meet connectivity needs [11; 14]. The automotive connectivity market share will continue rising up from the current 50% of CVs in retail to 95% worldwide sales in 2030 as estimated by McKinsey & Company [15]. Almost half of the sold CVs will include intermediate and advanced connectivity levels. The wide adoption of connectivity in the automotive industry will be considered significant when utilizing vehicle data to enhance safety, achieve cost optimization, and generate profit (up to an annual average of USD 310 income and USD 180 savings per vehicle in 2030). Monetizing connectivity to provide on-demand and customized connected features including subscribed in-vehicle hotspot and regular service check-ups based on the driver's usage are receiving significant attention by the automotive industries [16].  
The integration of connectivity into CVs will require underlying technologies to enable its deployment, functionality, and reliability within the intelligent transportation system (ITS) [17]. These enabling technologies will also facilitate the transmission and exchange of real-time data with high reliability as well as ensure smooth integration between smartphone devices and in-vehicle communication networks [14]. Minimal power requirements, cost, and scalability are critical factors that affect these technologies [11], especially in the case of electric vehicles (EVs). This section focuses on the enabling technologies for CVs and their current status to date.  
Many efforts have been carried out by researchers to enable wireless vehicular communication in dense, dynamic, and harsh weather environments [11; 18]. Wireless communication technologies currently used in connected vehicles mainly include dedicated short-range communications (DSRC), Wi-Fi 6, and cellular-vehicle-to-everything (C-V2X)communication. More ongoing research is exploring whether combining different communication technologies would result in a better connectivity for future CVs.
# A Searchable Encryption with Forward/Backward Security and Constant Storage
###### Abstract  
Dynamic searchable encryption satisfies users' needs for ciphertext retrieval on semi-trusted servers, while allowing users to update server-side data. However, cloud servers with dynamically updatable data are vulnerable to information abuse and file injection attacks, and current public key-based dynamic searchable encryption algorithms are often complicated in construction and high in computational overhead, which is not efficient for practical applications. In addition, the client's storage costs grow linearly with the number of keywords in the database, creating a new bottleneck when the size of the keyword set is large. To solve the above problems, a dynamic searchable encryption scheme that uses a double-layer structure, while satisfying forward and backward security, is proposed. The double-layer structure maintains a constant client-side storage cost while guaranteeing forward and backward security and further reduces the algorithm overhead by avoiding bilinear pairings in the encryption and decryption operations. The analysis results show that the scheme is more advantageous in terms of security and computational efficiency than the existing dynamic searchable encryption scheme under the public key cryptosystem. It is also suitable for the big data communication environment.  
searchable encryption; public key cryptosystem; forward security; backward security Article  
A Searchable Encryption with Forward/Backward Security and Constant Storage  
[PERSON], [PERSON], [PERSON], and [PERSON]  
1 College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China 1  
College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China 1  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
[EMAIL_ADDRESS]; Tel.: +86-183-041-2303
## 4 Evaluation  
The error correction capabilities that the Modulation and Coding Schemes (MCS) of the NB-IoT (and DVB-S2(X)) standard have on the end-to-end forward link of the GEO satellite system (i.e., from the satellite gateway to the IoT terminals) is now evaluated in detail. For this purpose, we first present the simulation setup and, after that, we show the different figures of merit that are relevant to characterize the end-to-end performance of the three GEO satellite relaying strategies.
# Optimized Downlink Scheduling over LTE Network Based on Artificial Neural Network
## 4 Proposed Model
### Proposed ANN Models for LTE Downlink Scheduling  
Solving these issues related to the allocations improves the video flow over the LTE network. We have described three ANN models used for normalizing the LTE system data. These included the approximate RBFNN (ARBFNN), the exact RBFNN (ERBFNN), and the generalized regression neural network (GRNN). There are three layers within the networks under the radial basis function neural network (RBFNN), including an input layer, a non-linear hidden layer with an activation function of RBF, and the linear output layer [26]. Furthermore, input modelling can be achieved as an absolute number vector (\(x\in\mathbb{R}^{n}\)). In contrast, the network output represents a scalar function from the input vector, \((\varphi:\mathbb{R}^{n}\rightarrow\mathbb{R})\) which is presented as Equation (9).  
\[\varphi(x)=\sum_{i=1}^{N}a_{i}\rho(\parallel x-c_{i}\parallel) \tag{9}\]  
where \((N)\) refers to the hidden layer neuron number, \((c_{i})\) denotes the neuron's center vector, \((i)\), and \((a_{i})\) indicates the neuron's weight \((i)\) used in the neuron's output linearly. Consequently, functions dependent on the center vector distance are symmetrically radial towards such a vector, resulting in the nomenclature \"radial basis functions\" [26]. Regarding the basic form, individual hidden neurons are linked to the inputs directly. The RBF is regarded in its Gaussian as Equation (10).  
\[\rho(\parallel x-c_{i}\parallel)=exp\Big{[}-\beta\parallel x-c_{i}\parallel ^{2}\Big{]} \tag{10}\]  
Figure 2: Proposed ANN Processing Framework Technique for Normalizing the Data.  
includes the pure line (PURELIN) neurons, which calculate the weighted input with the dot production (DOTPROD) and the net input with the net sum (NETSUM) [23]. The basis of the two layers is as follows:  
_\"P = P'; T = T\";\"\"_  
_\"eg = 0.02; sum-squared error target\"_  
_\"sc = 1; spread constant\"_  
_\"net2 = NEWRB (P, T, Spread), Exact NEWRBFNN\"_  
Model 3 uses the GRNN function approximation (NEWGRNN), which creates a two-layered network. Layer 1 comprises radial bases (RADBAS), neurons, and the distribution (DIST), which determines the weighted inputs, while the net production (NETPROD) calculates the net input. Layer 2 includes the pure line (PURELIN) neurons and determines the weighted input using the normalization product (NORMPROD), while the net input is determined using the net sum (NETSUM).  
_\"Spread = 0.7;\"_  
_\"net3 = newgrnn (P, Spread);\"_  
_\"A = sim (net3, P).\"_  
The testing model network replotted the training set and simulated the network responses for the input using the same range: (y-label, input vs. x-label, user) before an ANN model was proposed for the LTE downlink scheduling data (normalized data), wherein Figure 3 presents the output that has been tested and validated for deriving the results.  
Figure 3: Flowchart of the Validation Process for the Normalized Data.
## 3 Results and Discussion
### Microbiological Contaminants
The correlation analysis between total coliforms (MPN/100 mL) and pharmaceutical concentrations (Table S4, Supplementary Materials) revealed significant associations, suggesting a common anthropogenic origin of both microbiological and chemical contamination. The highest positive correlations were observed for piroxicam (PIR, r = 0.648), ketoprofen (KET, r = 0.621), and naproxen (NAP, r = 0.546), all nonsteroidal anti-inflammatory drugs (NSAIDs) frequently excreted in unmetabolized form. These findings suggest that the occurrence of such pharmaceuticals may serve as indirect indicators of recent fecal pollution in aquatic systems. Moderate correlations were also found for sulfamethoxazole (SMX, r = 0.453), enilconazole (IMZ, r = 0.502), 17\(\alpha\)-ethinylestradiol (EE2, r = 0.473), bisphenol A (BPA, r = 0.480), and amoxicillin (AMX, r = 0.570), highlighting the co-occurrence of antibiotics, antifungals, endocrine disruptors, and NSAIDs alongside fecal indicator bacteria. A multiple linear regression model (Table S4, Supplementary Materials) identified amoxicillin (AMX) and piroxicam (PIR) as significant predictors of total coliform concentrations, jointly explaining 80.4% of the observed variance (R\({}^{2}\) = 0.804; adjusted R\({}^{2}\) = 0.791). AMX and PIR resulted in strong predictive performance, possibly due to overlapping input sources (e.g., pharmaceutical residues from human excretion). Taken together, these resultssupport the hypothesis that both pharmaceutical and microbial contaminations share a common source, particularly from insufficiently treated effluents. As the sampling was carried out during the strict COVID-19 lockdown in 2020, which closed many activities, it is expected that the CECs levels recorded during this time interval were lower than in other periods [69]. This presumption, corroborated with the conclusion derived from another study related to the significant decrease in fecal pollution in all Danube sections in the period 2001-2019 [32], leads to the necessity of a more thorough monitoring of fluvial and marine chemical and microbiological pollution [34]. Moreover, pharmaceutics and heavy metals are widespread environmental contaminants considered potential markers of social and economic activities [70]. The scarcity and dispersion of the studies related to water-borne emerging and persistent pollutants and their synergies in lower Danube-Black Sea region [8; 33; 71] impose concerted seasonal monitoring campaigns in relevant sampling points.