esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Hybrid CEEMDAN-VMD-MPSO-BPNN Model  
To address the limitations of conventional corrosion prediction methods, which often struggle with information redundancy, noise interference, and capturing non-linear characteristics, a novel hybrid prediction model is proposed. The input parameters include hydrogen sulfide percentage, temperature, Cl\({}^{-}\), carbon dioxide percentage, PH, fluid flow rate, and corrosion inhibitor dosing and the output parameter is corrosion rate. This model leverages the strengths of several techniques: CEEMDAN-VMD, PCA, stratified sampling, BPNN, and MPSO. Figure 3 illustrates the integrated framework of this hybrid prediction  
Figure 2: The feed-forward neural network.  
model. In this model, the input parameters are first decomposed into several IMFs and residuals by CEEMDAN. Since the high-frequency IMF1 may increase the difficulties of forecasting, VMD is used to decompose IMF1. The pattern obtained by VMD, IMFs except IMF1 and residuals are used as input features. Next, the patterns obtained by VMD, IMFs except IMF1 and residuals are dimension-reduced by PCA, and the processing results are used as new input features. Then, the stratified sampling method is utilized to divide the data into a training set and test set, avoiding biases caused by random sampling. After that, a BPNN optimized by MPSO is adopted to predict the patterns after dimension reduction. The model's predictive performance is assessed using various indicators, including R\({}^{2}\), MAPE, RMSE, MAE, U1, U2, RE, forecasting effectiveness, Grey Relational Analysis, and interpretability research. All experiments are performed in Python 3.6.6 on Windows 10 with Intel Xeon E5-2643 v4 processor and 96.0 GB memory. The main experimental parameters are listed in Table 4.  
Figure 3: The flowchart of the hybrid forecasting model.
## 1 Introduction
Herein, in this paper, the refraction tomography method is optimized to improve the accuracy of inversion. Firstly, the T0 difference method was chosen for the prior constrained modeling for tomography. Then, a forward modeling method based on FMM is introduced which is called Multistencil fast marching method (MSFM). The accuracy and efficiency between FMM and MSFM are then compared and analyzed. Next, a composite constrained inversion method based on T0 difference method is proposed. Furthermore, we use dynamic regularization factors in inversion. We use the synthetic data for trial calculation and analysis, which results verify the effectiveness of the optimized travel time tomography algorithm. Finally, the refracted wave data of highway tunnel portal are processed, and two layers of velocity interface are found out from the results. The tunnel entrance is located at the speed interface. This problem should be considered in construction to prevent possible geological hazards at the entrance. At the same time, the practicability of the algorithm is proven, which can solve actual geological problems.
# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 6 Open Issues and Future Research Directions
### Real-Time Detection  
A key issue with video deepfake detection systems using CNN and LSTM is their computational expense, which hinders real-time analysis. The complexity of these models, coupled with the need to process vast amounts of video data, results in high latency, making them impractical for time-sensitive applications like live video moderation or surveillance [88]. The computational burden is further amplified when dealing with high-resolution or long-duration videos, where current models struggle to balance accuracy and efficiency. This presents a significant bottleneck, as delayed detection can reduce the system's effectiveness in mitigating the spread of harmful deepfake content [57]. Future research should focus on developing lightweight models or optimising existing ones for faster, more efficient processing without compromising detection accuracy. Techniques such as model compression, pruning, and quantisation could help streamline the computational load. Leveraging hardware acceleration, such as GPUs or TPUs, and edge computing to distribute processing closer to the data source could further enhance real-time capabilities. Additionally, exploring hybrid models that combine CNN and LSTM with faster architectures like transformers may offer a solution for achieving both speed and accuracy. These advancements are essential for making real-time deepfake detection feasible in practical, large-scale applications.
# Short-Text Semantic Similarity (STSS): Techniques, Challenges and Future Perspectives
## 1 Introduction  
In natural language processing, short text semantic similarity is considered the most essential technique. It has been used in a variety of applications such as network analysis, search snippets, question-answering systems (QAS), information retrieval, text categorization, and so on. Earlier, the methods calculated the similarity between longer sentences in a very high dimensional space; thus, the method used has been inefficient and cannot be effective for many NLP tasks [1]. However, the short text reveals information that is important to understand. The short text presents numerous distinct challenges for natural language processing in contrast to longer sentences. Shorter sentences have a larger likelihood of being difficult to understand [2]. These sentences contain many common buzzwords that create noise in the data. However, short text frequently encompasses polysemous and synonymous terms, one word can have several distinct meanings, and it is even possible that two or more than two words may have the same meaning or concept [3, 4]. These various aspects make it more complicated for machine learning to retained such information.  
In short text semantic similarity, a significant challenge is to identify the semantics of sentences based on the context [5, 6]. The context can be developed from many responses. These responses can be student answers, search queries, news headlines, tweets, and user feedback. However, machine learning algorithms still face difficulties in comprehending the meaning of words from text corpora due to the number of drawbacks of short sentences.
# Underwater 2D Image Acquisition Using Sequential Striping Illumination
## 4 Experimental Results
is twofold: Removal of the back scattered light by using the virtual aperture to each frame image effectively suppresses the back scattered background in the synthesized image; the subsequent image recovery eliminates the blur of the albedo image by collecting the forward scattered light. Moreover, the colored version of the image can be recovered, pleasing to the visual inspection. It is noticed that, since light attenuation underwater is wavelength depended, the colored recovered image can also infer the spectral nature of the medium. The experimental results show that the recovered object albedo looks reddish compared to its appearance in the air, implying that the red channel has better penetration in turbid water than green and blue channels, at least in the emulated medium in our experiments.
# Diamond-Like Carbon (DLC) Coatings: Classification, Properties, and Applications
## 2 Classification of DLC Coatings
### Properties
#### 2.4.6 Alloyed DLCs  
Novel properties of DLC are a promising way to meet the requirements for several applications, and normally they can be enhanced by alloying with other elements [77; 78; 79; 80; 81; 82]. An investigation of the impact of alloying with different transition metals; (for example, Ti, Cr, and Al), was conducted. Al, in particular, was found to reduce the stress and thereby permit the growth of thicker films. These can be referred to as a-C: Me or a-C: H: Me, separately. Alloying with transition metals is additionally used to expand the mechanical strength of the films. DLC is a rather brittle ceramic, and alloying with carbide-forming metals makes it harder as a result of the development of nano-sized carbide inclusions. Furthermore, the expansion of Si to a-C: H resulted in low stress and reduced friction coefficient in humid conditions [81]. In addition, after mixing of fluorine (F), the surface energy was found to be significantly low. However, it may vary if Si, nitrogen (N), and boron (B) are added [79]. The addition of N in films has been pointed out because of the hypothetical compound, i.e., C\({}_{3}\)N\({}_{4}\), where C is sp\({}^{3}\)and N is sp\({}^{2}\) bonded [80].
###### Abstract  
The frequent occurrence of global climate change and natural disasters highlights the importance of precision agricultural monitoring, yield forecasting, and early warning systems. The data assimilation method provides a new possibility to solve the problems of low accuracy of yield prediction, strong dependence on the field, and poor adaptability of the model in traditional agricultural applications. Therefore, this study makes a systematic literature retrieval based on Web of Science, Scopus, Google Scholar, and PubMed databases, introduces in detail the assimilation strategies based on many new remote sensing data sources, such as satellite constellation, UAV, ground observation stations, and mobile platforms, and compares and analyzes the progress of assimilation models such as compulsion method, model parameter method, state update method, and Bayesian paradigm method. The results show that: (1) the new remote sensing platform data assimilation shows significant advantages in precision agriculture, especially in emerging satellite constellation remote sensing and UAV data assimilation. (2) SWAP model is the most widely used in simulating crop growth, while Aquacrop, WOFOST, and APSIM models have great potential for application. (3) Sequential assimilation strategy is the most widely used algorithm in the field of agricultural data assimilation, especially the ensemble Kalman filter algorithm, and hierarchical Bayesian assimilation strategy is considered to be a promising method. (4) Leaf area index (LAI) is considered to be the most preferred assimilation variable, and the study of soil moisture (SM) and vegetation index (Vis) has also been strengthened. In addition, the quality, resolution, and applicability of assimilation data sources are the key bottlenecks that affect the application of data assimilation in the development of precision agriculture. In the future, the development of data assimilation models tends to be more refined, diversified, and integrated. To sum up, this study can provide a comprehensive reference for agricultural monitoring, yield prediction, and crop early warning by using the data assimilation model.  
remote sensing; crop growth model; data assimilation; precision agriculture +
Footnote †: journal: _Agronomy_  
18 July 2024  
Revised: 21 August 2024  
Accepted: 23 August 2024  
Published: 27 August 2024
###### Abstract  
Due to the micro-vibration of flywheels, the imaging quality of a high-resolution optical remote sensing satellite will be deteriorated, and the micro-vibration effect on the payload is complicated, so it is essential to establish a reasonable and accurate theoretical simulation model for it. This paper presents a method of full-closed-loop time-domain integrated modeling to estimate the impacts of micro-vibration generated by flywheels on optical satellites. The method consists of three parts. First, according to the satellites' micro-vibration influence mechanism in orbit, this paper establishes a full-closed-loop model framework. The overall model input is the instructions received and the output is the image shift. Second, in order to meet the requirements of time-domain simulation, this paper proposes a time-domain vibration source subsystem model in the form of cosine harmonic superposition, and it integrates vibration source, structural, control, and optical subsystem models to create a full-closed-loop time-domain analysis model that can obtain the responses of micro-vibration in time and frequency domains. Lastly, the author designs a ground experiment and compares simulation results with experiment results. Compared with the ground experiment, frequency error is less than 0.4% at typical responses. Although the amplitude error is large at some typical responses, the mean root square error is less than 35%. Based on the data, the proposed integrated modeling method can be considered as an accurate methodology to predict the impacts of micro-vibration.  
spacecraft micro-vibration; integrated modeling method; micro-vibration source model; full-closed-loop time-domain model; flexible satellite +
Footnote †: journal: Applied Sciences
## 2 Antenna Arrays
### Linearly Arranged Rectangular Antenna Array (Lkana)  
We show a rectangular beamformer in Figure 1b. The beamformer has a total of 108 elements, divided into three rectangular arrays of 36 elements each. Each rectangular side of the LkAA beamformer has six elements, for a total of 36 elements. The interval between two adjacent items is \(0.5\beta\). We present another linear beamformer in Figure 1c. The beamformer contains a total of 108 elements, divided into three circular arrays of 36 elements each. Each LCPAA has seven rows, with the upper three and lower three being symmetrical. The first, second, and third rows each have three, five, and seven items, respectively, while the middle row has six. Although the centered element raises only one element, it has a significant impact on the beam width and sidelobe level reduction. In Figure 1d, the LCCAA beamformer's system model is explained briefly. Three concentric circular antenna arrays with a central element are positioned linearly in this beamformer. As a result, the beamformer is named the linearly arranged concentric circular antenna array (LCCAA). There are 36 elements in each LCCAA beamformer with the central element. Each LCCAA is made up of three rings, with the inner ring containing six elements, the middle ring twelve, and the outermost ring seventeen, and all three rings sharing one central element. In this proposed beamformer, the total element is considered to be 108. The distance between two adjacent items is \(0.5\beta\). The central element of each ring in this suggested beamformer generates a linear structure with \(0.5\beta\) spacing between any two neighboring elements.
## 4 Comparison with Prior Research Works  
Table 8 presents a comprehensive comparison between the proposed multiple input multiple output (MIMO) antenna and previous studies in terms of bandwidth, bands, isolation, envelope correction coefficient (ECC), and diversity gain.  
According to the data shown in Table 8, it can be observed that the proposed antenna demonstrates the ability to operate over many frequency bands, while also improving the bandwidth with commendable envelope correction coefficient (ECC) and diversity gain. While the findings of [9] demonstrate similar characteristics with slightly higher bandwidth, the MIMO antenna proposed in this study exhibits lower ECC and greater value in terms  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline ECC & \textless{}0.004 \\ \hline Diversity gain & \textgreater{}9.99 dB \\ \hline Mean effective gain & \textless{}1.2 dB \\ \hline \hline \end{tabular}
\end{table}
Table 7: MIMO diversity performance.  
Figure 19: Simulated and measured mean effective gain curve.  
of multiband features compared to the structure described in the literature [9]. The antenna under consideration in this study offers enhanced system capacity and MIMO performance compared to the antenna configuration described in [9].
# Upregulation of Neuronal Rheb(S16H) for Hippocampal Protection in the Adult Brain
## 2 Characteristics of Rheb  
Rheb proteins, monomeric proteins of approximately 21 kDa, are highly conserved during evolution, and their expression is found in yeast and humans [14]. Rheb consists of 184 amino acids; the N-terminal 169 amino acids make up the GTPase domain, and the 15 remaining C-terminal residues make up a highly variable region ending in a CAAX motif. The crystal structures of Rheb bound with GTP, GppNHp, or GDP have been determined [14]. The structure of Rheb is highly similar to that of other small GTPases, with a closer resemblance to those of Ras and Rap than to those of Rab5A and RhoA [14]. In addition, due to the unique structure of Rheb, Gln64 (corresponding to Gln61 in Ras) is buried in a hydrophobic core and cannot interact with either GTP or the catalytic active site. Another difference compared with Ras is that a conserved tyrosine residue (Tyr35) in Rheb (corresponding to Tyr32 in Ras) shields the phosphate moiety of GTP. These two unique structural features of Rheb suggest that the mechanism of GTPase in Rheb differs from that in Ras. Recent findings have clarified the role of Rheb in activating mTORC1 signaling at the lysosomal membrane [31; 32]. Two different complexes interact with each other on the lysosomal membrane. One of the complexes contains mTORC1, which is localized in lysosomes by the action of a heterodimeric GTPase (Rag) [33; 34]. Rag consists of RagA/RagC or RagB/RagD. The binding of Rag to the lysosomal membrane requires Regulator, which consists of multiple proteins including p18, p14, and MP1. The other complex is tuberous sclerosis complex (TSC)/Rheb, which is localized in lysosomes via a farnesyl group on Rheb. The co-localization of Rheb and mTORC1 facilitates the activation of mTORC1 by Rheb. Further insights into the regulation of these lysosomal events can be gained from recent studies that investigated the signals affecting the localization of these proteins [35; 36]. Insulin causes the dissociation of TSC and the release of TSC from lysosomes, resulting in the activation of Rheb [35]. On the other hand, amino acid starvation does not affect the lysosomal localization of TSC2. Since amino acid starvation affects mTORC1 localization, it is suggested that insulin signals through TSC/Rheb, while amino acidssignal through Rag and mTORC1. However, Rag GTPase binds to TSC2, and this binding is stimulated by amino acid starvation [36]. Thus, further studies should reveal the interplay between two different signals that converge on the lysosomal membrane.
## 4 Discussion  
### The Effect of Single Parameters on TPC  
Based on results from preliminary experiments, all the experimental parameters (the ethanol concentration, the extraction temperature, and the extraction time) facilitated the extraction of a phenolic substance from black goji berry via the ASE in a certain parameter range. The extraction time and temperature were the dominant factors. The best conditions of these single factors were 90 \({}^{\circ}\)C and 11 min at 60% ethanol. For the extraction temperature, possible reasons for the TPC increase at 50 to 90 \({}^{\circ}\)C were the improved solubility and higher diffusion rates of the extracting solvent and other compounds [10]. The unique ability of ASE to maintain the extraction at a high temperature and a high pressure of 10 Mpa prevented the degradation of extract compounds [16]. In addition, the extraction was promoted due to better contact between analytes and a solvent because of reduced viscosity of solvent and decreased surface tension of a solvent, solute and a matrix at higher temperatures [17]. The following decrease of extraction efficiency may owe to the thermal degradation of TPC when the temperature is too high. On the other hand, the improved extraction time also promoted the extraction of TPC which was possibly owing to the required time of the process that the solute was exposed to the release medium where the liquid penetrated the sample, dissolved the solute and subsequently diffused out from the sample [18].
## 2 Materials and Methods
### The Estimation of Optical Radiation Losses with EIM and the Payne-Lacey Model
After the LER and RMS determination, optical radiation losses could be evaluated with the Payne-Lacey model, which was initially developed for symmetric 2D slab waveguide [39; 40]. The Payne-Lacey model may be used for studying optical losses in 3D waveguides while applying EIM to obtain a 2D slab equivalent to the original waveguide and approximating the 3D radiative modes with 2D planar radiative modes [35; 38]. The accuracy of estimation of radiation losses by the Payne-Lacey model directly depends on the calculation accuracy of the effective refractive index by the EIM; as a result, it can also be used only for waveguides with those cross-sectional parameters for which the EIM has an acceptable calculation error.
## 3 Results and Discussions
### Systematic Distribution of Measurement Points
* High-quality mass production: Where consistency and speed of measurement are key to maintaining quality standards at high production volumes. * Automotive and aerospace: In areas where very high precision and repeatability of measurements are critical to safety and efficiency. * Advanced technology research and development: Where innovative designs require precise, repeatable measurements in complex conditions or on small, delicate components. The objective was to identify the differences between these three devices and determine to what extent a device with a higher measurement error can replace a more precise device. Increasing the number of measurement points makes the results obtained on these devices more similar. The use of random distribution of measurement points aimed to determine the impact of uneven point distribution on the final result. This work demonstrates that in some industries, where tolerances do not need to be too stringent, less precise instruments may prove to be sufficient. In such cases, these instruments allow for cost, time, and resource reduction required for measurements. The authors explored whether less precise devices could be accepted under industrial conditions. It is also worth noting that
# Preparation Techniques of TiO\({}_{2}\) Nanofluids and Challenges: A Review
## 2 Preparation Methods
### Single Step Method
#### 2.1.2 Laser Ablation Method  
This is a technique used to disperse the nanoparticles from the surface of the material submerged in the base fluid by irradiating it with a highly focused laser beam. The depth of the submerged material depends upon its optical properties, the amount to be removed and the laser intensity and wavelength. [PERSON] and [PERSON] [40] prepared TiO\({}_{2}\) nanofluid under a nitrogen-free atmosphere by submerging the anatase type TiO\({}_{2}\) into the flowing fluid as shown in Figure 4.  
Figure 4: Pulsed laser ablation (PLA) model schematic diagram [40].  
They used three different base fluids (anhydroous ethanol, cyclohex, water) during the experimental study and found that anhydrous-ethanol was the best flowing fluid under conditions of low flow rates (0.017-0.15 mL/s). Supreme pulsed laser output power (100-250 mJ/pulse) and with appropriate aging time, nano-TiO\({}_{2}\) ethanol colloids were obtained with strong fluorescence emission intensity around 414 nm.  
[PERSON] et al. [41] used a titanium plate with a diameter of 15 mm and thickness of 2 mm to formulate TiO\({}_{2}\) nanofluid by using laser ablation method while continuously stirring the plate with the help of rotator. The titanium plate was placed inside the glass vessel filled with base fluid (polyvinylpyrrolidone). To meet the experimental requirements, the laser beam was focused on the titanium plate by passing it through the quartz lens of focal length 250 mm.  
[PERSON] et al. [42] formulated TiO\({}_{2}\) nanofluid by placing the titanium plate into a solution of deionized water (7 cm\({}^{3}\)) and a varying quantity of surfactant (0.001 M, 0.01 M, and 0.1 M). A laser beam was focused on the dipped plate with a maximum output power of 150 mJ/pulse via a lens of focal length of 250 mm that vaporized the plate and nanoparticles were spread into the base fluid. The solution of TiO\({}_{2}\) prepared by adding SDS (0.01 M), endured cluster formation and remained stable for more than a week.  
This is a much more reliable, cost-effective and efficient technique compared to numerous traditional time consuming and multi-step processes.