esa-sceva/satcom-chunk-collection huggingface dataset:
# Glucose Isomerase: Functions, Structures, and Applications
## 1 Introduction  
Glucose isomerase (GI, EC 5.3.1.5; also known as D-xylose ketol isomerase, xylose isomerase (XI), xylose keto isomerase, and xylose ketol-isomerase) is widely distributed in bacteria, actinomycetes, fungi, and plants [1,2]. This enzyme is an intramolecular oxidoreductase that can interconvert aldoses and ketoses. GI occupies a pivotal position with respect to its physiological role and commercial applications [3]. It is one of the three most commonly produced industrial enzymes, along with amylase and protease [2,3]. In particular, this enzyme is extensively used in the industrial production of high-fructose corn syrup (HFCS) [2]. Moreover, the bioconversion of xylose to ethanol by GI is important for the production of bioethanol from hemicellulose [4]. In this review, I introduce the molecular function, structural characteristics, and industrial applications of GI and describe the recent crystallographic results related to GI structure.
## 4 Discussion  
Fine dust can have serious effects on health, causing respiratory diseases, weakening the immune system, and affecting crop growth. To reduce fine dust over large developed or agricultural areas, it is more efficient to use crops than machinery or equipment. Fodder crops, which can grow even in barren and rough environments, can reduce airborne dust by coagulating soil particles and reducing wind speed on the surface and have the effect of reducing fine dust by adsorbing it on the crop surface. In this study, the PM reduction coefficient of crops was derived through a chamber-type wind tunnel experiment to quantitatively assess the dust reduction effect of crops by type and planting density. Additionally, a PM reduction formula considering crop growth and weather conditions during the cultivation period was derived.  
A small wind tunnel was designed and manufactured to establish stable environmental conditions following the environmental conditions obtained through field experiments. The PM reduction coefficient was measured by two experiments: the gravimetric method, which weighs the dust adhered to the leaves washed with distilled water using a filter, and measuring the real-time diameter distribution of dust concentration before and after the crop. Based on the experimental results, the effect of reducing fine dust during the cultivation period was calculated, considering the dust reduction coefficient, rain frequency, and the canopy of the crops.  
Figure 11: Accumulation of fine dust reduction per hectare according to crop type, monthly canopy and rainfall frequency. **(a)** Triticale (PM-10), **(b)** triticale (PM-2.5), **(c)**, barley (PM-10) **(d)** barley (PM-2.5).  
During the cultivation period, the PM reduction amount was calculated using the rainfall frequency, crop canopy, and PM reduction coefficient. According to the gravimetric method, the dust reduction coefficient of the crops, with moderately sowing as a standard, showed trticale at 2233 g/ha and barley at 1839 g/ha. The crop-specific PM reduction coefficient calculated through real-time concentration analysis showed trticale at PM-2.5 82.2 mg/m\({}^{3}\), PM-10 120 mg/m\({}^{3}\), and barley at PM-2.5 14.5 mg/m\({}^{3}\), PM-10 26.9 mg/m\({}^{3}\) under general planting density. Triticale, having relatively more hairs than barley, showed higher PM reduction coefficients, 8.3 times for PM-2.5 and 3.1 times for PM-10 at general sowing intervals. For the experimental target area, PM reduction coefficients, the canopy varying with crop growth, and rain frequency were used to calculate the cumulative dust reduction amount due to crop cultivation. During the general planting density cultivation period of trticale and barley, PM-10 was reduced by 37.8 kg/ha and 8.5 kg/ha, respectively, and PM-2.5 was reduced by 25.9 kg/ha and 4.6 kg/ha, respectively. The dust reduction effect during the cultivation period was up to 126.1 kg/ha in terms of PM-10 when trticale was cultivated with densely sowing planting density.
# Lightweight Transformer with Adaptive Rotational Convolutions for Aerial Object Detection
## 2 Background Research
### Multi-Scale Feature Fusion  
Aerial imagery often contains objects of varying sizes, posing an additional challenge for detection models. Multi-scale feature fusion techniques, such as those employed in Feature Pyramid Networks (FPNs) [23], have been shown to improve detection accuracy across diverse object scales. Existing methods like Oriented R-CNN and RoI Transformer incorporate multi-scale features to enhance robustness. However, they often fail to appropriately weigh the importance of features from different scales, leading to suboptimal performance, particularly for small or densely packed objects.  
RASST addresses this limitation by incorporating a Multi-Scale Feature Fusion (MSFF) module that employs cross-scale attention to adaptively weigh features from different scales. This ensures that the most informative features are prioritized, enabling the model to achieve high accuracy in detecting objects of varying sizes.  
Despite the progress made, significant limitations remain in the field. Many existing methods for oriented object detection rely on fixed rotation anchors or face computational inefficiencies when employing dynamic rotational mechanisms. Semi-supervised frameworks frequently struggle with noisy pseudo-labels, limiting their effectiveness in scenarios with limited labeled data. Additionally, multi-scale feature fusion methods often overlook the importance of adaptive weighting, which is crucial for detecting small or densely packed objects effectively.  
By integrating rotationally aware mechanisms, refining pseudo-labels through novel loss functions, and leveraging multi-scale feature fusion with lightweight attention mechanisms, RASST provides a comprehensive solution to these challenges. This innovative architecture not only achieves SOTA performance but also establishes a new benchmark for robustness and scalability in oriented object detection. Through its contributions, RASST significantly advances the capabilities of object detection in aerial imagery.
###### Abstract  
This report describes a technique integrating facial and dental scanners for treatment planning and execution of a tooth-borne full-mouth reconstruction (FMR) with zirconia fixed prostheses. A partially edentulous female adult presented generalized worn dentition in the incisal and occlusal surfaces. The patient accepted the option for treatment with a fixed FMR solution using a fully digital workflow. An intraoral scan (IoS) for both arches and facial scans collected the initial clinical situation. A digital diagnostic wax-up was generated as part of the treatment plan, and the models were printed. The intervention included crown lengthening of the maxillary teeth aided by a printed guide, installing milled provisional restorations, and delivering permanent milled zirconia partial restorations, single crowns (SCs), and fixed dental prostheses (FDPs). The functional extraoral scanner permitted designing an FMR that created a harmonious dental, gingival, and facial relationship with the patient's eshetic consent approval. IoS captured the initial clinical situation to design (CAD) and fabricate (CAM) the PMMA provisional and zirconia final partial restorations, SCs, and FDPs. Facial and intraoral scanners are technologies that can synergistically assist oral health providers in the diagnosis, planning, and execution of fixed FMR using an entirely digital workflow.  
facial scanner; dental scanner; CAD/CAM; digital workflow +
Footnote †: journal: Medical Sciences  
1 Clear Choice Dental Implant Center, Tampa, FL 33610, USA 12 Escuela de Odonologia, Universidad de Especialidades Espiritu Santo, Guayaquil 092301, Ecuador 2  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 1 Introduction
This type of stimulation is known as the steady-state visual-evoked potential (SSVEP) paradigm and can be used to control brain-computer interfaces [38]. In the study of cognitive processes such as attention, the oddball paradigm is often used. In this case, event-related potentials are investigated, mainly the P300 component, which is a positive deflection in EEG signal and usually occurs about 300 ms after the stimulus presentation. The P300 is an attention-related component and is considered an indicator of active detection of targets by the subject [39; 40]. Further research has shown that P300 can be separated into two subcomponents, P3a and P3b [41; 42], and due to its characteristics, P300 can also be applied to brain-computer interface communication [43]. Considering the results of the literature review, as observed, ASR is regarded as a method with the potential to provide promising results. However, it is still treated as a relatively new method which needs to be explored [31]. The idea of the study presented in this paper is to check if the process of automation of artifact removal, more and more willingly applied in the area of cognitive neuroscience, can affect patterns in the data. The ICA is a well-known, widely applied method which is considered to be safe for EEG activity patterns. We decided to check if adding another method, ASR, in the data-preprocessing phase could change these patterns. Such an influence, if detected, could have a negative impact on data and cause incorrect results. The ASR, as added to the procedure, was conducted before the ICA, as ASR is performed continuously on small segments of the data whereas ICA should be used on continuous data. The ASR is a non-stationary method using a sliding window, while ICA is a stationary method. The ASR applied before ICA removes less reproducible data discontinuities and prepares the signal for ICA application. The experimental procedure and the research group used in this study are typical for cognitive neuroscience research. It is worth mentioning that this case study covers the most popular experimental procedures in order to verify the process of preprocessing automation. We did not attempt to perform a direct assessment of the algorithm parameters or apply modified versions of the methods. Instead, we used default values of parameters as they are suggested by the methods developers and applied in other research papers. Another factor worth mentioning is the computational cost, which is an important aspect of the assessment of both approaches as it has a practical influence on the applicability of both solutions, so this aspect was also taken into account in the paper.
# Precise Mission Process Control Based on a Novel Dual-Code Group Network Plan Diagram
## 3 Dual-Code Group Network Plan Diagram Model with Time Line
### Network Plan Diagram Modeling
The projection of node \(k\) on the time axis \(t_{k}\) represents the time scale of the node. The scales of the mission start node \(S_{m^{i}_{j}}\) and the mission end node \(E_{m^{i}_{j}}\) on the time axis represent the start moment \(T^{m^{i}_{j}}_{s}\) and the end moment \(T^{m^{i}_{j}}_{f}\) of the mission \(m^{i}_{j}\), respectively, i.e.,  
\[T^{m^{i}_{j}}_{s}=t_{S_{m^{i}_{j}}} \tag{5}\]  
\[T^{m^{i}_{j}}_{f}=t_{F_{m^{i}_{j}}} \tag{6}\]  
The dotted lines in the network plan diagram represent dummy missions, whose mission durations are 0, and are only used to represent cooperative relationships among missions. As far as the mission \(m^{i}_{j}\) is concerned, all the missions pointing to the start node \(S_{m^{i}_{j}}\) represent the front missions of \(m^{i}_{j}\), and all the missions flowing out from the end node \(E_{m^{i}_{j}}\) represent the post missions of \(m^{i}_{j}\). The mission \(m^{i}_{j}\) can only start when all the prior missions of \(m^{i}_{j}\) are finished. If the set of prior missions for mission \(m^{i}_{j}\) is \(\mathcal{EM}_{m^{i}_{j}}\) and the set of post missions is \(\mathcal{FM}_{m^{i}_{j}}\), we can obtain  
\[ \begin{split}\mathcal{EM}_{m^{i}_{j}}&=\{m_{e}\},m^ {i}_{j}\in\mathcal{FM}_{m_{e}}\\ \mathcal{FM}_{m^{i}_{j}}&=\Big{\{}m_{f}\Big{\}},m^ {i}_{j}\in\mathcal{EM}_{m_{f}}\end{split}  \tag{7}\]  
Unlike single network plan diagrams, for group network plan diagrams, the executors work in parallel, which leads to the global start node and global end node of the plan diagram, which cannot be determined. Therefore, without changing the logical relationship between missions, we set up a global start node \(S\) and a global end node \(F\), which are represented by red and blue colors, respectively, in Figure 6. The time scale \(t_{S}\) of the global start node \(S\) and the time scale \(t_{F}\) of the global end node \(F\) are the minimum and maximum values of all node scales, respectively. Assuming that the set consisting of all the nodes of the network plan graph is \(\mathcal{N}\), then  
\[t_{S}=\min\{t_{k}\},k\in\mathcal{N} \tag{8}\]  
\[t_{F}=\max\{t_{k}\},k\in\mathcal{N} \tag{9}\]  
Figure 5: Dual-code group network plan diagram model with timeline. In summary, two improvements have been made to the traditional dual-code network plan diagram. Firstly, the description of mission time progress is achieved by setting up a time axis. The second is that the group network plan diagram is converted into a single network plan diagram by setting up global start nodes and global end nodes. In this way, the characteristics of traditional network plan diagrams are retained and the cooperative relationship between missions is also represented. Therefore, the dual-code group network plan diagram with timeline achieves the complex plan description with multiple executors and multiple constraints.
# ANN Sizing Procedure for the Day-Ahead Output Power Forecast of a PV Plant
## 3 Methodology
### Data Set Definition  
In order to perform the forecast, historical data are provided to the Neural Network Toolbox(tm) [20] in Matlab software, R2016a, MathWorks(r), Natick, MA, USA, and grouped in three clusters: \"training\", \"validation\", and \"test\". Each group fulfills three specific tasks:  
1. The _training set_ includes the samples employed to train the network. It should contain enough different examples (days) to make the network able to generalize its learning.
2. The _validation set_ contains additional samples (i.e., days not already included in the training set) used by the network to check and validate the training process.  
(3) The _test set_ is the dataset corresponding to the days actually forecasted by the network.
# Current Trends in Fully Automated On-Line Analytical Techniques for Beverage Analysis
## 6 On-Line Fully Automated Extraction-Chromatography-Mass Spectrometry
[91] used an on-line system composed of an SPE-LC-ESI-TOF-MS system, two switching valves and an auxiliary pump to load the sample. In this work, designed for analyzing sulfonamide antibiotics in bovine milk, the authors employed an ionic liquid supported on silica synthesized via a sol-gel approach. This material showed a high permeability as its main advantage, which made it adequate for on-line coupling at low backpressures. This fully automated method showed low detection limits (1.5-2.25 ng/mL) and reproducibility. [PERSON] et al. [89] employed a packed column with graphene oxide supported on silica, for the analysis of triazines in bottled, mineral and groundwater using an in-tube SPME-UHPLC-MS/MS on-line system. This material exhibited high extraction capacity, good mechanical and thermal strength, and high-pressure resistance. The method was adequate, getting good sensitivity, linearity and with very low detection limits (1.1-2.9 ng/L). In spite of the samples analyzed not being contaminated with triazines, the method showed it could adequately work with these matrices.
## 1 Introduction
The primary challenge in applying ANNs, FLNNs, and GAs in BTC lies in the diversity and inconsistency of methods, datasets, and evaluation metrics. Additionally, the lack of standardized datasets and preprocessing techniques complicates the assessment of model generalizability. This systematic review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [10]. The study aims to consolidate the current body of knowledge in this field by extracting and analyzing key data from 53 relevant studies. It examines various types of neural networks, fuzzy logic-based models, genetic algorithms, the datasets used, segmentation approaches, and evaluation metrics. This review provides insights to guide future BTC research and development by identifying common trends, research gaps, and challenges. This study aims to answer the following questions:  
* What are the trends in applying ANNs for segmentation tasks, as reflected in the types of ANNs, datasets, and segmentation approaches used in the recent literature? * How do different training and testing mechanisms and evaluation metrics correlate with performance outcomes (e.g., accuracy, precision) in ANN-based segmentation models across the selected papers? Unlike previous systematic reviews that primarily focus on specific models, this study uniquely integrates and compares artificial neural networks, fuzzy logic-based neural networks, and genetic algorithms within the same systematic framework. Moreover, it contributes by classifying studies into conceptual and practical categories, detailing the train-test mechanisms, segmentation approaches, and evaluation metrics. The remainder of this paper is organized as follows: Section 2 explains the study's methodology. Section 3 presents the descriptive analysis of the selected studies. Section 4 provides qualitative insights and discusses key findings. Finally, Section 5 concludes the research with recommendations for future work.
## 4 Time-Frequency Analysis for Feature Extraction
### Hilbert-Huang Transform (HHT)  
The HHT is a method for TFA that addresses nonlinear and non-stationary signals, devised by Dr. [PERSON] in the 1990s. The HHT consists of two principal components:
# Denoising Multiscale Back-Projection Feature Fusion for Underwater Image Enhancement
## 1 Introduction
The former focuses on retaining useful information, whereas the latter aims to boost image denoising. In addition, we design a multiple degradation knowledge distillation strategy to extend our method for multitype degradation image enhancement. The primary contributions of this paper can be summarized as follows:  
1. Novel denoising back-projection feature fusion mechanism: We present a novel denoising back-projection feature fusion mechanism to effectively integrate multiscale features in underwater image enhancement. This mechanism significantly improves the quality of enhanced images by filtering noise and preserving fine details. 2. Multiple degradation knowledge distillation strategy: We introduce a multiple degradation knowledge distillation strategy, enabling our method to generalize across various types of degradation images. 3. We evaluate the model on different datasets against other underwater enhancement models. The results indicate that the proposed method achieves the best performance for underwater images with complex degradation. The results of knowledge distillation validate the effectiveness and applicability of our method to address multiple degradation scenarios. In the following, we first review the related work on underwater image enhancement and then introduce the proposed method in detail. After evaluating the proposed method, we will conclude the work in the final section.
## 4 Applications of AI in Modern Communication Networks
### Latency Reduction Comparison  
In this section, I compare the latency performance of traditional methods and AI-optimized methods. The AI-optimized methods significantly reduce latency compared to traditional approaches. The following bar chart demonstrates this comparison.  
Figure 5 demonstrates a comparison between traditional network management and AI-optimized methods for reducing latency, with AI-based approaches achieving a significant reduction.  
Figure 4: Comparison of dynamic bandwidth allocation between AI-based reinforcement learning (RL) and traditional methods across allocation stages.  
Figure 5: Latency reduction comparison between traditional and AI-optimized methods.  
AI-based models are also used for efficient resource allocation [102]. By analyzing usage patterns and predicting demand fluctuations, AI can optimize the distribution of network resources, such as server capacity or bandwidth, ensuring that resources are utilized efficiently and costs are minimized [96].  
Table 5 summarizes the efficiency improvements achieved through the application of various AI models in resource allocation.
# Genetic Algorithms Optimized Adaptive Wireless Network Deployment
## 3 Problem Formulation
### Fitness Computation  
Assume that each UAV has a limited initial energy of \(10^{6}\) joules and consumes energy while hovering/moving and providing data to users [22]. Intuitively, if more UAVs share the bandwidth demanded by users, the per UAV bandwidth service can be reduced, leading to less energy consumption and longer UAV flight times. Thus, the aim is to deploy UAVs to increase the bandwidth coverage while sharing bandwidth demands. Each UAV is classified as either an active UAV (AU) or an inactive UAV (IU) based on whether or not the UAV is serving a user. If a UAV finds users within its ground-sensing range, \(U_{gr}\), the UAV is an AU; otherwise, it is an IU. The number of AUs is proportional to the network lifetime. The more AUs, the more bandwidth that can be distributed among AUs, leading to less power consumption per AU and thus a longer battery life to stay aloft and keep the network alive. A variable \(\alpha_{i}\in\{0,1\}\) represents the status of UAVs, where \(\alpha_{i}\) is 1 for active UAVs and 0 for inactive UAVs.  
\[f_{bw}=\sum_{i=1}^{N}u_{bi} \tag{1}\]  
\[f_{a}=\sum_{i=1}^{N}\alpha_{i} \tag{2}\]  
\[Maximize\quad f=\left[\begin{array}{c}\frac{f_{bw}}{\frac{m}{j}}+\frac{f_{ a}}{N}\\ \frac{1}{j=1}\end{array}\right] \tag{3}\]  
Equation (1) calculates the aggregate bandwidth that is provided to users, where \(i\) sums over \(N\) UAVs and \(u_{bi}\) denotes the bandwidth served by the \(i\)th UAV. The objective function value \(f_{bw}\) increases as the bandwidth coverage expands. In the same vein, Equation (2) tallies the total number of active UAVs, \(f_{a}\), in the deployed network. As the number of \(f_{a}\) increases, the per AU data rate decreases and the energy consumption decreases as well. To ensure that the bandwidth coverage lasts for an extended period, \(f_{bw}\) and \(f_{a}\) are combined in Equation (3) to form the fitness function, where \(b_{j}\) is the bandwidth requirement of the \(j\)th user and \(m\) is the number of users.  
It is worth mentioning that the first and second terms of the fitness function were normalized due to the uncertainty surrounding the maximum bandwidth demand from users and the number of available UAVs for deployment. By normalizing the components of the fitness function, the maximum and minimum values are always confined within a fixed range. The maximum value of each term in the Equation (3) is 1, which limits the fitness value to a range of [0-2]. When computing the objective function using Equation (3), several constraints must be satisfied. Each UAV must be positioned within the specified AOI; thus, the UAV's \(x\) and \(y\) coordinates must be \(\in(0,2000)\). Our simulation used the \(xy\) plane for the horizontal plane, \(u_{bi}\leq u_{bmax}\), where \(u_{bmax}=5\), which is the maximum bandwidth a UAV can provide in Mbps [22].