esa-sceva/satcom-chunk-collection huggingface dataset:
# Texture Analysis of Near-Infrared Vein Images During Reactive Hyperemia in Healthy Subjects
## 4 Discussion
### Study Limitations and Future Directions  
A key limitation of this study is the small and homogeneous sample, composed exclusively of healthy young adults. While sufficient to assess intra-subject dynamics and methodological feasibility, this limited cohort restricts the generalizability of our findings. Caution is therefore warranted when extrapolating these results to broader or clinical populations. Additionally, while the composite indices proposed in this study were based on physiological reasoning and observed signal dynamics, they were constructed heuristically. No multivariate techniques were applied, as the limited sample size would not support stable dimensionality reduction. Future studies with larger cohorts should explore data-driven approaches to validate or refine these indices. Future research should apply the proposed texture-based parameters to diverse populations, particularly those with known vascular dysfunction such as diabetes or chronic venous insufficiency. Additionally, longitudinal studies could assess the stability and sensitivity of these parameters over time, supporting their potential use as non-invasive biomarkers for vascular health monitoring.
# Carbon Nanotubes-Based Hydrogels for Bacterial Eradiation and Wound-Healing Applications
## 2 Conductive Properties of CNTs
### Functionalization of CNTs  
Nanoparticles and their structure with intrinsic chemical compositions would enhance the surface properties such as variable charge, dispersion, dissolutions, hydrophilicity, hydrophobicity, etc. Many studies and techniques are available for the functionalization of CNTs materials [53; 54; 55]. The functionalization of CNTs can be categorized as covalentant non-covalent strategies [51]. The covalent modification adds chemical bonds on CNTs by chemical reactions and conjugates hydrophilic molecules or polymers, which increases the solubility of CNTs. Whereas, in non-covalent modification, the hydrophobic behavior of CNTs is used for coating amphiphilic molecules. Thus, the desirable functionalization of CNTs is not a difficult task, which is beneficial for desirable biological or biomedicine feasibility [17; 50]. The covalent functionalization generates more hydroxyl and carboxylic groups on the surface of CNTs. The non-covalent functionalization has been performed through the adsorptions of desired moieties, including polymers, proteins, DNA, carbohydrates, and hydrogels with CNTs [20]. Most of these surface modification techniques are helpful for the utilization of functionalized CNTs material in biomedical applications.  
The functionalized moieties can be precisely fabricated on CNTs' surface cell receptors that can go through their internalization. Despite the vast advantage of CNTs, some concerns are to be addressed before biomedical applications. These concerns are (i) the purity of CNTs and their structural reproducibility in large scale synthesis, (ii) the physicochemical and structural properties of CNTs are varied significantly vary with synthesis, purification, and functionalization methods, (iii) the co-operation between the biological environment and CNTs are not always predictable and very complex, (iv) the toxicity of CNTs is a most critical issue, which depends on the synthesis methods and modifications, (iv) the design and synthesis of desired CNTs-based material on a large scale, and (v) hydrophobicity [17]. In addition, they suggested functionalization strategies that can help in minimizing systemic toxicity and inflammation. Usually, CNT materials have been utilized to combine with hydrogels (non-conductive polymers) to form electroactive composite dressings for wound-healing treatment [12]. The combination of CNT and hydrogel renders excellent electro-conductive properties in the resultant composites, which can effectively transmit electrical stimulation to the wounded tissue. In addition, these CNTs-hydrogel composites exhibit an antibacterial phenomenon. Hence, this promising combination could effectively heal wounds by activating local cell proliferation and migration.
## 3 Results  
### Switchable Terminator Design  
In order to obtain functional terminators amenable to switching, we screened strong rho-independent terminators that satisfy the following characteristics: (a) hairpin loop size as small as 4-5 nt, (b) stem-bottom sequences composed of strong GC pairing, and (c) the stem-loop structure followed by the long poly-U tract [43; 44; 45; 46]. We found two candidate terminators satisfying the above design criteria and aimed to modify them with additional engineered toehold domains analogous to the STAR library [30]. As an example, a natural terminator BBA K864600 was selected from the _E. coli_ forward terminator library within the iGEM Parts. BBA K864600 showed strong transcription termination strength with an intuitive and short stem-loop structure among the 24 _E. coli_ forward terminators (Figure S2) [47; 48]. Additionally, a synthetic terminator, T500, was selected due to its very short hairpin structure and a termination efficiency of close to 98% [45]. T500 terminator has been widely used in synthetic biology applications as an ideal terminator exhibiting a significant elongation complex (EC) dissociation rate [49; 50; 51]. Thus, the BBa K864600 and T500 terminators, with their strong transcription termination strengths and short stem-loop structures, can represent ideal targets to be engineered as SWT.  
To facilitate hybridization with trigger RNA molecules, we engineered toehold domains upstream of these terminators (Figure 2a and Figure S3). We employed the toehold sequence of STAR target RNA for the SWT and designed cognate trigger RNA such that it can initiate hybridization with SWT at the toehold domain and continue branch migration to disrupt the terminator stem-loop formation. In the absence of trigger RNA, the SWT inhibits downstream gene expression through a rho-independent termination mechanism; however, in the presence of trigger RNA, the RNA-RNA interaction between SWT and cognate trigger RNA disrupts the terminator hairpin formation, allowing transcription of the downstream gene. A functional SWT obtained via modifying the T500 terminator showed a very low leakage level close to that of autofluorescence while allowing strong GFP expression in its ON state (Figure 2b).  
Figure 2: Design Schematics of Switchable Terminator (SWT) and GFP Fluorescence Histogram. (**a**) Design schematics of SWT, where transcription terminators are engineered with an additional toehold domain to facilitate interaction with designed trigger RNA molecules. In the absence of a trigger RNA, the rho-independent terminator is formed, preventing further transcription elongation. In the presence of trigger RNA, the trigger RNA initiates hybridization with SWT at the toehold domain (a–a*) and continues branch migration to unwind the stem-loop structure of SWT, allowing transcription elongation via RNAP. (**b**) Flow cytometry GFP fluorescence histograms for SWT based on T500 synthetic terminator in the presence of cognate trigger RNA or decoy RNA. autofluorescence level was measured from cells not bearing a GFP-expressing plasmid.
# Mental Workload Assessment Using Machine Learning Techniques Based on EEG and Eye Tracking Data
## 3 Materials and Methods
### Brief Overview of Machine Learning Algorithms
#### 3.5.1 K-Nearest Neighbors (KNN)  
The k-nearest neighbor (k-NN) algorithm is widely used in pattern recognition or classification tasks. Its approach involves analyzing the k-nearest training samples in the problem space to ascertain the class of a new, unlabeled data point. The k-NN algorithm operates under the assumption that points with similar features are likely to belong to the same class. KNN is categorized as an instance-based learning method as it depends on the specific instances or examples in the training dataset to classify new data points [48].
# Radiometric Calibration of the Near-Infrared Bands of GF-5-02/DPC for Water Vapor Retrieval
## 5 Discussion
In conclusion, the validation results based on both the AERONET and SuomiNET data demonstrate that calibrating the DPC at the 865 nm and 910 nm bands is indeed beneficial for reducing the error of the DPC PWV data. In addition to the DPC, there are several sensors with NIR bands used for PWV retrieval, such as MERSI and MODIS. Due to systematic calibration errors in the sensors, the official MERSI PWV data and MODIS PWV data also exhibit significant error [12; 21]. In the future, the calibration method developed for DPC at NIR bands can also be applied to the above sensors to reduce the retrieval error of existing PWV data. Figure 9: The distribution of the absolute error of DPC PWV data. (**a**) The distribution of the absolute error of data M1. (**b**) The distribution of the absolute error of data M2. The dashed line is the linear regression line. The red points are the means for specific ranges of absolute error, and the red lines are the mean \(\pm\) 2\(\sigma\) (standard deviation) of absolute error in a certain range.
## Abstract  
**(Aim)** Classification of brain images as pathological or healthy case is a key pre-clinical step for potential patients. Manual classification is irreproducible and unreliable. In this study, we aim to develop an automatic classification system of brain images in magnetic resonance imaging (MRI). **(Method)** Three datasets were downloaded from the Internet. Those images are of T2-weighted along axial plane with size of 256 \(\times\) 256. We utilized an \(s\)-level decomposition on the basis of dual-tree complex wavelet transform (DTCWT), in order to obtain \(12s\) \"variance and entropy (VE)\" features from each subband. Afterwards, we used support vector machine (SVM) and its two variants: the generalized eigenvalue proximal SVM (GEPSVM) and the twin SVM (TSVM), as the classifiers. In all, we proposed three novel approaches: DTCWT + VE + SVM, DTCWT + VE + GEPSVM, and DTCWT + VE + TSVM. **(Results)** The results showed that our \"DTCWT + VE + TSVM\" obtained an average accuracy of 99.57%, which was not only better than the two other proposed methods, but also superior to 12 state-of-the-art approaches. In addition, parameter estimation showed the classification accuracy achieved the largest when the decomposition level \(s\) was assigned with a value of 1. Further, we used 100 slices from real subjects, and we found our proposed method was superior to human reports from neuroradiologists. **(Conclusions)** This proposed system is effective and feasible.  
magnetic resonance imaging; parameter estimation; support vector machine; dual-tree complex wavelet transform; twin support vector machine; variance; entropy  
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detection, because it does not need to identify each subject but identify the status (pathological or healthy). Hence, it is expected that our methods can achieve high classification accuracy.  
The difference of accuracy in Table 4 is not significant, however, it is obtained by strict statistical analysis, viz., 10 runs of \(K\)-fold stratified cross validation. Hence, this slight improvement is reliable and convincing. Even the largest dataset only contains 255 images, so we will try to create a larger dataset that contains more images and more types of diseases.  
The proposed CAD system cannot give physical meanings of particular brain regions. Nevertheless, after comparing classifier and human brains, we believe expert systems are similar to declarative memory, while support vector machines are similar to nondecarative memory. Thus, it is impossible for SVMs (its variants) to give physical meanings. In the future, we may try to use expert systems that can mimic the reasoning process of doctors, but may not give as high accuracies as SVMs.
# Intelligent Transmission Control Scheme for 5G mmWave Networks Employing Hybrid Beamforming
## 3 System Model  
The proposed intelligent transmission control scheme is designed for a 5G mmWave network environment characterized by hybrid analog-digital beaming, multiple-user nodes, and an intelligent reflecting surface (IRS) for non-line-of-sight (NLOS) conditions. The mmWave base station is provided with a hybrid analog-digital beamforming mechanism, and the user devices are equipped with adaptive antenna arrays for directional transmission.  
Network components  
Base station (BS): We used a 4-element hybrid beamforming antenna array, which was tasked with pointing signals at multiple users. User node (UN): All users were fitted with a 100-element antenna array, which can facilitate adaptive beamforming to provide robust communication links. When direct transmission is disrupted, IRSs backscatter the BS signals toward consumers.
## 3 Results and Discussion  
### Microbiological Characteristics of Raw Meat, Hybrid, and Plant Burgers  
A very useful and effective strategy for preventing or reducing the incidence of pathogens, thereby improving food safety and consumer health, can be the application of beneficial lactic acid bacteria (LAB), mainly from the following genera: _Lactobacillus_, _Carnobacterium_, _Pediococcus_, _Lactococcus_ and _Enterococcus_[17; 18]. LAB can be used either as protective cultures (live microorganisms that inhibit the development of pathogens and/or extend the shelf life of products) or as probiotic cultures (live microorganisms that, when consumed, can have a beneficial effect on health). The protective role of LAB has been proven in many non-fermented food products as part of scientific research [36; 37; 38] or industrial practice. The LAB cultures (_Lactobacillus sake_) used in this research for the bioprotection of raw hybrid and plant burgers have the ability to multiply at low temperatures, which allows them to dominate the microbiological environment and effectively combat _Listeria monocytogenes_ bacterium [39; 40]. The results of microbiological analyses of raw burgers are presented in Table 2.  
The aerobic colony count (ACC) varied from 9.2 \(\times\) 10\({}^{4}\) to 1.6 \(\times\) 10\({}^{5}\) cfu/g. Different proportions of meat and plant parts and the recipe variants did not have a statistically significant impact on ACC variability. According to international guidelines [41], ACC for comminted raw meat should be lower than 10\({}^{7}\) cfu/g for cooked vegetables and cereals <10\({}^{5}\) and <10\({}^{6}\) cfu/g, respectively. Therefore, taking into account the heterogeneous composition of the tested hybrid burgers, it can be concluded that they met the microbiological requirements relating to the permissible number of aerobic colony-forming units in 1 g of the semifinished product. In turn, the LAB counts, ranging from 9.4 \(\times\) 10\({}^{6}\) to 1.8 \(\times\) 10\({}^{7}\) cfu/g, were typical (expected) due to the dose of these protective cultures used before the cold storage of raw burgers (Table 2).
## 2 Proposed Framework and Experimental Methodology
### Proposed Encryption Workflow with Hybrid CPU-GPU Strategies  
This hybrid approach balances workload distribution effectively, maximizing parallel processing on GPUs while efficiently leveraging CPU strengths for sequential tasks, small block encryption, and metadata processing. Prior research has demonstrated that parallel AES algorithms implemented on CPU-GPU heterogeneous platforms can significantly enhance energy efficiency, highlighting the practicality and relevance of hybrid encryption approaches [22].
# Ellipsoidal K-Means: An Automatic Clustering Approach for Non-Uniform Data Distributions
## 2 Adaptive Ellipsoidal Clustering Technique
### Assignment of Points  
\(K\)-means clustering is typically used to assign points to the nearest centers to form clusters. However, a new approach is proposed here to replace \(K\)-means for point assignment, as \(K\)-means tends to group points into spherical shapes, particularly when dealing with nested clusters.  
The foci of an ellipse, denoted as \(F_{1}\) and \(F_{2}\), are two special points located on the ellipse's major axis and are equidistant from the center. For a point \(x_{i}\) on ellipse cluster \(k\), the sum of the distances from \(x_{i}\) to both foci must be less than or equal to the length of the major axis, as shown in Equation (14) where the distance from the center \(c_{k}\) to each focus is \(f=\sqrt{a^{2}-b^{2}}\).  
\[\|x_{i}-F_{1}\|+\|x_{i}-F_{2}\|\leq 2a. \tag{14}\]  
We can now determine whether a point \(x_{i}\), where \(i=1,\ldots,m\), belongs to the cluster \(k\) based on Equation (14). The calculation of \(F_{1}\) and \(F_{2}\) can be performed using the following equation, which leverages the properties of unit vectors discussed earlier.  
\[F_{1}^{i}=\frac{(x_{max}^{i}-c_{k}^{j})\times f}{a}+c_{k}^{i},\quad\forall \quad i=1,\ldots,m,\]  
where \(x_{max}\) is the point that is at a maximum distance \(a\) from the center \(c_{k}\), according to Equation (4).  
Figure 4: **(a) Illustration of the computed two perpendicular vectors \(r\) on the major vector \(\bar{v}\) of the cluster in three dimensions. There are also two symmetrical (reflected) vectors for \(r\). (b) Illustration of how to calculate the optimal minor axis \(b\) for any cluster, ensuring that the ellipsoidal cluster does not interfere with an established spherical cluster if the ellipsoidal cluster’s diameter exceeds the exact minor axis.**  
The symmetric (reflected) point of \(F_{1}\) with respect to the center \(c_{k}\) can be computed as \(\bar{F}_{2}=2\bar{c}_{k}-\bar{F}_{1}\).  
Equation (14) is used to assign points in the dataset and generate \(K\) clusters. It is essential to first identify the minor and major distances. Any point that satisfies Equation (14) will then be assigned to its respective cluster.
## 4 Results and Discussions
### Thermal and Exergy Efficiencies Assessment under the Weather Conditions of Budapest  
In this section, the assessment of the energy and exergy efficiencies of the PTC model showed the effect of inserting HNFs at various volumes, side by side, with the radiation intensity in Budapest was used as a case study. Figure 12 expresses the optical efficiency and the incident angle modifier for the different angles used in this assessment because of its importance in expressing a daily simulation of this collector. The results show an inverse relationship between the optical efficiency values and the increase in values of angles where the maximum optical efficiency is achieved at angle 0\({}^{\circ}\) and equals 74.5%. The assessment of the thermal and exergy efficiencies for the chosen days (21 June and 21 September) over time are presented in Figure 13 and Figure 14, respectively. The volume flow rate and the inlet temperature were constant and equal to 150 L/min and 500 K. In contrast, the ambient temperature and radiation intensity changed over time for the volume concentrations of 0, 2% and 4%. Figure 13 presents the thermal efficiency obtained over time for typical winter and summer days. It was found that the maximum thermal efficiency was observed at  
Figure 11: Exergy efficiency for base fluid and hybrid nanofluid under different temperatures and concentrations.  
Figure 10: Thermal efficiency for base fluid and hybrid nanofluid under different temperatures and concentrations.  
midday for both typical days, and the maximum value reached in the summer equaled 71.255%, with an enhancement equal to 0.26% at a concentration 4% compared with the base fluid. Figure 14 demonstrates the exergy efficiency for the same days and times. It was found that the maximum exergy efficiency was observed at midday for both typical days, and the maximum value reached in summer equaled 32.728%, while it reached 30.732% in winter with an average enhancement equal to 0.25% at a concentration 4% compared with the base fluid. These results can be justified as shown in Figure 10 and, as well as in previous research [53]. For more details, Table 7 shows the obtained results for the useful heat, loss heat, exergy, and energy efficiencies at midday of the chosen days (21 June and 21 September), and all concentrations of the proposed hybrid nanofluid. The table shows an increase in heat gain using nanofluids, but there is no great variance between the concentrations; the maximum heat gain was observed at a volume concentration of 4% and equaled 25,371 W compared with the base fluid that reached 25,300 W.  
Figure 12: Incident angle modifier and optical efficiency for the different angular values.  
Figure 13: Thermal efficiency of the base fluid and HNFs for sunny and winter days in Budapest.  
On the other hand, the heat losses for the nanofluid concentrations compared with Therminol VP1 showed decreases, with heat losses that reached 1239 W at high concentrations. The major results showed a maximum enhancement equal to 0.25% for both efficiencies at the highest concentration and at midday.
###### Abstract  
Stereophonic audio devices employ two loudspeakers and two microphones in order to create a bidirectional sound effect. In this context, the stereophonic acoustic echo cancellation (SAEC) setup requires the estimation of four echo paths, each one corresponding to a loudspeaker-to-microphone pair. The widely linear (WL) model was proposed in recent literature in order to simplify the handling of the SAEC mathematical model. Moreover, low complexity recursive least-squares (RLS) adaptive algorithms were developed within the WL framework and successfully tested for SAEC scenarios. This paper proposes to apply a data-reuse (DR) approach for the combination between the RLS algorithm and the dichotomous coordinate descent (DCD) iterative method. The resulting WL-DR-RLS-DCD algorithm inherits the convergence properties of the RLS family and requires an amount of mathematical operations attractive for practical implementations. Simulation results show that the DR approach improves the tracking capabilities of the RLS-DCD algorithm, with an acceptable surplus in terms of computational workload.  
adaptive filters; data-reuse (DR); dichotomous coordinate descent (DCD); recursive least-squares (RLS) algorithm; system identification; stereophonic acoustic echo cancellation (SAEC); widely linear (WL)
# An Intelligent Fuzzy-Based Routing Protocol for Vehicular Opportunistic Networks
## 3 System Model and Design of the Proposed Protocol  
This section presents the problem formulation, system model, and the design of the proposed fuzzy-based routing protocol. We begin by defining the key challenges and motivations for this work, followed by a description of the underlying system model and assumptions. Finally, the details of the proposed protocol, including its input parameters, fuzzy logic system, and decision-making process, are discussed.
# A Privacy-Preserving Protocol for Utility-Based Routing in DTNs
## 4 System Design of PPUR
### Security Analysis
#### 4.5.1 Passive Attack  
A passive adversary (eavesdropper) may not interfere with the protocol, but instead monitors the underlying communications to get some relevant privacy information. PPUR can prevent malicious nodes from acquiring meaningful private information by overhearing the encounter record information. Firstly, the encounter record information \(\text{ET}_{ij}\) is encrypted by the session secret key Key\({}_{ij}\) of nodes \(N_{i}\) and \(N_{j}\), and the key can only be obtained by nodes \(N_{i}\), \(N_{j}\), and TA, so the eavesdropper cannot understand the content in the transmitted encounter record information. Secondly, when receiving a message, if a node is not the destination node, it can anonymously compare utility values with the encountered node directly through the solution for \"Yao's Millionaire Problem\". If a node is the destination node, it sets the utility value to the max value of 1 and compares utility value with the solution for \"Yao's Millionaire Problem\", so the destination node can get the message and ensure anonymity at the same time. As a result, the eavesdropper cannot determine the ID of a node based on the messages it receives.