esa-sceva/satcom-chunk-collection huggingface dataset:
# Interactions between Phase-Separated Liquids and Membrane Surfaces
## 4 Phase Separation Mediated by Membranes Can Reshape and Laterally Reorganize Membranes  
### The Formation of Some Biomolecular Condensates is Mediated by the Membrane  
Because the surface of the membrane represents a distinct environment from free solution, liquid phases may form there that do not form otherwise. Biomolecular condensates form when component concentration crosses the binodal line of the phase diagram [11], and membranes are known to locally affect protein concentrations: Proteins with no specific membrane interactions are excluded from the membrane surface and concentrated in bulk [42], and proteins may be concentrated at specific membrane sites through signaling-related protein-protein interactions [43] or curvature-sensing membrane-binding domains [44], for example. The protein FtsZ forms a biomolecular condensate and mature condensates do not reside at the membrane. However, when protein is encapsulated in a water-in oil droplet stabilized by phospholipids under non-condensing conditions, small structures emerge at the membrane boundary, indicating the membrane may act as a nucleation site [45]. Phase-separating proteins that can associate with (or are integral to) the membrane can form flat, lateral domains on the membrane surface. For such systems, the critical concentration for phase separation is often much lower than that for the same components in solution [46; 47]. The formation of many biomolecular condensates, therefore, is mediated by the presence of a membrane, and these biomolecular condensates exhibit distinct physical interactions from droplets that phase separate from bulk solution in the absence of a membrane.  
Figure 3: **(a)** A budding vesicle encapsulating phases \(\alpha\) and \(\beta\) and surrounded in phase \(\gamma\). On a macroscopic scale, three apparent contact angles (\(\theta_{\alpha}\), \(\theta_{\beta}\), and \(\theta_{\gamma}\)) may be measured at the point where the boundary between liquid phases contacts the membrane; **(b)** The membrane is in fact locally smooth at this intersection; the intrinsic contact angle (\(\theta_{\text{in}}\)) of the \(\alpha\)-droplet with the membrane is the angle between the tangent planes to the membrane and to the liquid phase boundary. **(c)** During membrane engulfment of a nanodroplet, symmetry is broken; the line tension is negative so a long contact line minimizes line energy while the contact area between the separate liquid phases is reduced, creating a “lipped” membrane neck. The plane of the x-z cross section is shown by the dotted line in the y-z cross section and vice versa. The plane of the membrane is shown in x-y. Dark green represents the lipid bilayer and light green the droplet; **(a,b)** reproduced from [PERSON], [PERSON], [PERSON], and [PERSON] [38].
###### Abstract  
Overlapped Time Domain Multiplexing (OvTDM) is a high-rate transmission technology that employs the idea of superposition coded modulation (SCM) scheme for signal generation, aiming to achieve maximum channel capacity sharing. Meanwhile, it is also widely considered as a promising technique toward physical layer security. As a main drawback of such system, a high peak-to-average power ratio (PAPR) issue in this system, arising from multi-layer superposition, can be addressed through intentional clipping. However, the detection at the receiver side is vulnerable to nonlinear distortion caused by clipping, which can degrade the performance. To mitigate this distortion, this paper proposed an iterative scheme for estimating and partially canceling clipping distortion at the receiver. We managed to mitigate the impact of clipping noise as much as possible and minimize the cost of optimizing PAPR, thereby improving the transmission performance of OvTDM in the context of amplitude clipping.  
clipping distortion mitigation; Overlapped Time Domain Multiplexing (OvTDM); superposition coded modulation (SCM); peak-to-average power ratio (PAPR) Article
# Sharpening of Worldview-3 Satellite Images by Generating Optimal High-Spatial-Resolution Images
## 2 Materials and Methods
### Proposed Pansharpening Technique
#### 2.3.2 Sharpening of VNIR Bands with 1.2 m Spatial Resolution  
In the second phase of the proposed pansharpening technique, the VNIR bands and SWIR bands with 1.2 m spatial resolution produced in the first phase were sharpened with the panchromatic image, which had a 0.3 m spatial resolution. To do this, the VNIR bands with 1.2 m spatial resolution and the SWIR bands were stacked. The stacking of the two bands is defined by Equation (12):  
\[\mathbf{VNIR}^{\prime}=\left[\mathbf{VNIR},\ \widehat{\mathbf{SWIR}}\right] \tag{12}\]  
After the bands were stacked, general pansharpening using panchromatic images was conducted using the CS-based GSA technique and the MRA-based GS2 technique. For the GSA technique, multiple regression analysis was applied to a panchromatic image that was transformed into the number of pixels of the VNIR image and the \(\mathbf{VNIR}^{\prime}\) bands to calculate the regression coefficient to generate the optimal low-spatial-resolution image \(\mathbf{I}_{L}\) using Equations (13) and (14):  
\[\mathbf{P}_{L}=\omega_{0}+\sum_{n=1}^{16}\omega_{n}\mathbf{VNIR}_{n}^{\prime} \tag{13}\]  
\[\mathbf{I}_{L}=\omega_{0}+\sum_{n=1}^{16}\omega_{n}\mathbf{VNIR}_{n}^{\prime} \tag{14}\]To apply the GSA technique, the injection gain \(g_{n}\) was generated through the covariance of \(\mathbf{I}_{L}\) and \(\mathbf{VNIR}^{{}^{\prime}}\) and the variance of \(\mathbf{I}_{L}\), as presented in Equation (15):  
\[g_{n}=\frac{cov\big{(}\mathbf{I}_{L},\mathbf{VNIR}^{{}^{\prime}}\big{)}}{var \big{(}\mathbf{I}_{L}\big{)}} \tag{15}\]  
The optimal low-spatial-resolution image \(\mathbf{I}_{L}\) used in the GS2 technique can utilize images whose spatial resolution has been reduced by applying the MTF filter to the panchromatic image. The injection gain \(g_{n}\) is calculated using Equation (22) in the same way as the GSA technique. The result of applying the GSA and GS2 techniques to the Worldview-3 satellite images is defined by Equation (16).  
\[\widehat{\mathbf{VNIR}}^{\prime}=\widehat{\mathbf{VNIR}}^{\prime}+g(\mathbf{ P}-\mathbf{I}_{L}) \tag{16}\]  
The final sharpened image was produced by applying the spectral distortion reduction technique using the sharpened and original images generated through the technique proposed in this study. After applying the existing pansharpening techniques, the results were used in comparative evaluations to assess the performance of the sharpening results of the Worldview-3 satellite images. The existing method sharpens the VNIR and SWIR bands using panchromatic images by applying the GSA and GS2 algorithms.
## 3 Literature Review
### Personalisation of the User Interface in HAVs
[37] showed that models such as GPT-4 improve the recognition of autonomous driving commands, allowing for easier and clearer communication between passengers and the vehicle. This capability helps to reduce cognitive load by improving the vehicle's ability to understand and respond appropriately to natural language commands. Similarly, [PERSON] et al. [38] carried out an analysis of the use of LLMs for hybrid agility in the context of autonomous driving, with a particular focus on challenging weather conditions. The simulations in CARLA demonstrate that the AI-based personalisation approach has higher performance outcomes compared to human performance in certain scenarios. Additionally, [PERSON] et al. [21] presented DriveMLM, a framework that unifies the decision-making process and employs a multimodal language model to enhance the interpretation of sensory data and driving rules. In the same line of thought, [PERSON] et al. [39] proposed a safety framework where LLMs are adopted to strengthen data-protection mechanisms and ensure regulatory provisions are satisfied to overcome the shortcomings of the existing safety systems. These inventions highlight the growing role of LLMs in improving the functionality and safety of HAV systems. Although these emerging technologies have the potential to be transformative, significant challenges remain in terms of effectively implementing custom interface systems in real-world applications. Most work focuses on specific aspects, such as user interaction or real-time adaptation, without discussing how these components can be integrated into a coherent and reliable system. This omission hinders the creation of truly adaptive interfaces that would consider parameters such as user behaviour, the contextual parameters of the system, and environmental impact. While there are clear benefits to customisation, there are also risks associated with it, particularly in relation to privacy and data collection. A considerable number of studies are focused on enhancing user comfort. However, they offer limited guidance on achieving an optimal balance between personalisation and data protection. It can be reasonably argued that achieving this equilibrium is of the highest importance to maintain user confidence in autonomous driving systems. Furthermore, there is currently no consensus on the standards for personalisation. While some scholars argue that the standardisation of certain elements is crucial to guarantee security and consistency, the lack of such standardisation may lead to inconsistent user experiences and potential security vulnerabilities. This paper, therefore, tries to fill the existing gaps by offering a comprehensive understanding of personalisation in the interfaces of autonomous vehicles. This paper aims to propose a more holistic and detailed framework that can harmonise personalisation, standardisation and safety. The approach is, hence, expected to improve users' experiences and foster growing confidence in the autonomous driving systems.
# Candidate Term Boundary Conflict Reduction Method for Chinese Geological Text Segmentation
## 2 Related Work
### CWS Technology in the Geological Field  
Digital utilization of geological texts is mostly based on the recognition and extraction of geological named entities and knowledge services are provided by building knowledge graphs. However, lots of context information is lost in the process of extracting knowledge entities, which usually contain more domain knowledge. It is very necessary to achieve both knowledge extraction and named entity recognition through word segmentation.  
In the geographical field, [PERSON], relying on a geological dictionary, carried out word segmentation research on mineral texts based on the characteristic of the dictionary and CRF model [3]. [PERSON] designed a geological entity recognition model based on the Deep Belief Network, which can achieve better performance on the basis of a small-scale corpus [22], but there are still some text information recognition errors in the existing experiments. [PERSON] built a basic geological dictionary based on the prime string to improve the adaptability of statistics-based segmentation methods in geological text segmentation, but he could not recognize the new words that appeared only once and in the exactly same context [23]. [PERSON] et al., established the ELMO-CNN-BiLSTM-CRF model, which improved the accuracy of geological entity recognition in small-scale corpus and could effectively identify long geological entity words and geological polysemous words [24], but misclassification and missing characters occurred. Generally relying on geological dictionaries, statistics-based approaches and deep-learning-based approaches have been continuously applied to geological texts. Based on the small-scale corpus, good word segmentation results have been achieved, which can basically recognize terms and common words in the geological field. In general, CWS in the geological field has long relied on and lagged behind the development of word segmentation technology in the general field.
## 3 Tests Results and Analyses
### Sealing Effect  
The whole process of water flow change was detected during the simulated rough fracture grouting. The sealing effect can be used as the evaluation criterion of grouting plugging. After the  
Figure 10: The change curves of flowing water. (**a**) Single platform decreasing type. (**b**) Double platform decreasing type. (**c**) Multi-peak fluctuating type. (**d**) Multi-peak fluctuating type. growting finished and stabilized, the ratio of the reduction of water flow to the initial water flow was defined as the water sealing effect (\(SE\)). The formula of \(SE\) was put forward by [PERSON] (1965), as follows:  
\[SE(\%)=\frac{Q_{0}-Q_{grout}}{Q_{0}}\cdot 100 \tag{5}\]  
where \(Q_{0}\) is the initial water flow before grouting and \(Q_{grout}\) is the water flow after grouting. A classification system for the grouting effect was proposed by [PERSON] (1999), as shown in Table 3.  
Table 4 showed the results of the orthogonal test of chemical grouting in a rough fracture with flowing water. Trial number 14 which had a \(JRC\) of 18-20, an initial water flow speed of 0.8 cm/s, a gel time of 60.2 s, and a fracture opening of 2 mm, had the largest water plugging rate and the best plugging effect. Trial number 4 which had a \(JRC\) of 2-4, an initial water flow speed of 1.6 cm/s, a gel time of 83.6 s, and a fracture opening of 5 mm, had the smallest water plugging rate and the worst plugging effect. The value of sealing effect ranged from 6.82% to 93.75% across the whole test. Table 5 lists the range of influences on the sealing effect for different variables, \(SE_{\text{i}}\) (\(i=1\), 2, 3, 4) indicates the average values for factors with the same level. The factors and levels used in the experimental are shown in Table 1. \(SE_{1}\) represents the average value of sealing effect with the first level of each factor. \(SE_{2}\) represents that the average value of sealing effect with the second level of each factor. For example, when \(JRC\) = 2-4 was the first level of fracture roughness, the \(SE_{1}\) of the fracture roughness was obtained by calculating the average of trials 1, 2, 3 and 4, as shown in Table 5. The value of 0.4 cm/s was the first level of the initial water flow speed, so the \(SE_{1}\) of the initial water flow speed was obtained by calculating the average of trial 1, 5, 9, and 13 as shown in Table 5. The difference between the maximum and minimum values of diverse levels with same fac
## 3 Experiment
### Result Descriptions of Performance Indicators  
#### 3.3.1 Psnr  
PSNR is the most commonly used and widely recognized metric for measuring the similarity between a denoised image and a reference image. It is based on the error between corresponding pixel points, making it an error-sensitive image quality evaluation. Its calculation is as follows:  
\[PSNR=10\log_{10}(255^{2}/MSE) \tag{8}\]  
where \(MSE\) represents the mean squared error between the current image \(X\) and the reference image \(Y\).
Sensors_**2015**, _15_, 408-421; doi:10.3390/s150100408  
_Series of_  
ISSN **1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
In-to-Out Body Antenna-Independent Path Loss Model for Multilayered Tissues and Heterogeneous Medium  
**[PERSON] *, [PERSON], [PERSON], [PERSON] and [PERSON]**  
Ghent University-iMinds, Deptartment of Information Technology Gaston Crommenlaan 8 box 201,  
Ghent B-9050, Belgium; E-Mails: [EMAIL_ADDRESS] (G.V.);  
[EMAIL_ADDRESS] (E.T.); [EMAIL_ADDRESS] (W.J.);  
[PERSON]ESS] (L.M.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +32-9-33-14908; Fax: +32-9-33-14899.  
Academic Editor: [PERSON]  
_Received: 15 October 2014 / Accepted: 18 December 2014 / Published: 29 December 2014_  
**Abstract:** In this paper, we investigate multilayered lossy and heterogeneous media for wireless body area networks (WBAN) to develop a simple, fast and efficient analytical in-to-out body path loss (PL) model at 2.45 GHz and, thus, avoid time-consuming simulations. The PL model is an antenna-independent model and is validated with simulations in layered medium, as well as in a 3D human model using electromagnetic solvers.  
**Keywords:** path loss model; layered medium; antenna gain in conducting medium; WBAN; human body
###### Abstract
In order to enhance the algorithm's performance in dynamic scenarios, LSTM-D3 QN is proposed by replacing the fully connected layer of input states in the D3 QN with LSTM network. 3. Thirdly, we compare the proposed algorithm with other DRL algorithms and verify the effectiveness and convergence of our proposed algorithm. An extensive comparison of the utility of the system and the QoE of the users with and without the assistance of the DC technique verifies that the users and the network system, with the assistance of DC, have higher QoE and throughput in most cases. Then, we compare the impact of different numbers of users in the environment on different optimization objectives to obtain the number of users that our network system can accommodate, without reducing the QoE of users. Finally, we simulate and analyze the effect of different parameters on the performance of the algorithm. The remainder of the paper is organized as follows. Section 2 discusses the related literature. The system scenario is modeled and the optimization problem is formulated in Section 3. The foundation of D3 QN and the proposed algorithm of the network slicing resource allocation based on LSTM-D3 QN are presented in Section 4. The simulation parameters and results are discussed in Section 5. Contributions are discussed in Section 6. This work is concluded in Section 7.
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 3 Classification and Evolution of Interference Management Strategies in B5G Networks
### Classification and Analysis of Interference Management Strategies in B5G Networks  
Interference has a significant impact on the availability and reliability of wireless communication systems. To effectively manage the complex interference environments in B5G networks, various strategies have evolved over successive mobile generations. This subsection builds on that overview by classifying and analyzing the core IMI strategies considered in this review.  
The classification of IM issues into four main categories: D2D communication, HetNets, ICI, and AI-based frameworks is based on practical deployment challenges and current standardization efforts in B5G networks. These categories are selected because they represent the primary sources of interference affecting system performance and scalability, as recognized in ongoing 3 GPP studies [54; 55; 56; 57]. D2D communication introduces interference due to direct transmissions sharing licensed spectrum with cellular links [58; 59]. HetNets, composed of multi-tier architectures (e.g., macro-cell, micro-cell, and SC), experience both co-tier and cross-tier interference due to dense and uncoordinated deployments [60; 61]. ICI occurs in all densely packed cellular environments and disproportionately impacts cell-edge users under universal FR [62; 63]. Finally, AI-based frameworks approaches are increasingly vital for enabling RT, adaptive IMI in complex and dynamic network conditions [64; 65]. This classification reflects both industry priorities and recent research trends, enabling a structured and comprehensive evaluation of interference challenges and mitigation strategies in B5G systems.
## 5 Conclusions  
In this study, an innovative automatic procedure to differentiate PRP from SSc and HC based on machine learning algorithms and IR images of the hands was presented. Different classification procedures were compared, involving the use or not of a cold challenge and the manual or automated features selection. The results revealed that the use of a cold challenge did not statistically improve the classification accuracy and that an automated feature selection approach performed better than the manual ones. Indeed, this study demonstrated that, by employing a DCNN model and IR images of the hands in basal condition, it is possible to achieve high level of accuracy and specificity in the differentiation among PRP, SSc, and HC. This approach allowed for overcoming issuesrelated to the administration of a cold stimulus to the patients, as well as to avoid biases in the classification introduced by the operator, thus representing a great improvement with respect to the standard procedure. However, due to the limited sample size, this study is intended to provide evidence of the feasibility of such an automated approach. Further studies are needed to corroborate the results on a large-scale population. Although in literature it is reported that thermographic examinations are useful for differentiating secondary RP, such as SSc, from PRP, to the best of our knowledge this is the first study employing such an automated approach for this task. Finally, the implemented model, together with the recent introduction of low-cost thermal systems, can provide a new, quick, automated, and accurate classification method suitable for everyday clinical practice environment.  
Conceptualization, A.M., P.A., and C.F.; methodology, A.M., P.A., C.F., D.C., D.P., and A.M.C.; software, C.F.; validation, D.C., D.P., A.M.C., and G.G.; formal analysis, C.F., D.C., and A.M.C.; investigation, C.F., D.C., D.P., and A.M.C.; resources, A.M., P.A., and G.G.; writing--original draft preparation, C.F., D.C., and D.P.; writing--review and editing, A.M., P.A., G.G., and A.M.C.; supervision, A.M., P.A., and G.G.; project administration, A.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board and Local Ethical Committee of the School of Medicine of the University of Chieti-Pescara (protocol code AC052514 12/07/2016).  
Informed Consent Statement: Informed consent was obtained from all participants involved in the study. Written informed consent has been obtained from the patients to publish this paper.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy issues.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Review on Retrospective Procedures to Correct Retinal Motion Artefacts in OCT Imaging
## 3 Methods
### Validation Techniques
#### 3.5.2 Quantitative Metrics  
An accurate evaluation of the registration performance requires a quantitative and objective analysis. The proposed methods can be roughly divided into techniques that take into account the information available in the whole image, or in specific regions of interest.  
_Intensity-based_: Intensity-based metrics quantitatively compare the images using information contained in all voxels. The most commonly used intensity-based metric in OCT registration is mutual information (MI) [45].  
_Comparison of annotated structures_: These metrics quantitatively compare certain regions of interest in the image. There are two main approaches: measuring the overlapping of areas or volumes, such as the optic disc; and measuring the distance between structures, such as the edges of a given retinal layer in consecutive B-scans. These techniques may use a manual segmentation as reference. In OCT registration, the most commonly used technique to compute overlapping is the Dice similarity coefficient [52], and the most common choice for distances is the root mean square error (RMSE) with respect to a manual reference.