esa-sceva/satcom-chunk-collection huggingface dataset:
# Fourier Neural Operator Network for Fast Photoacoustic Wave Simulations
## 3 Results
### Image Reconstruction  
The reconstructed PAT images from the FNO network and k-Wave simulations were almost visually identical (Figure 5). Vasculature structures and artifacts arising from the limited-view nature of the linear array can be seen in both the k-Wave and FNO network images. The reconstructed images were quantitatively compared using the RMSE and the structural similarity index metric (SSIM), a metric ranging from 0 to 1 that measures the similarity between two images based on factors relevant to human visual perception (e.g., structure, contrast and luminance) [44]. For the testing dataset (\(n=100\)), the similarity between the FNO network and k-Wave images were measured with the RMSE (\(7.1\pm 1.5\times 10^{-3}\)), SSIM (\(0.98\pm 0.01\)) and maximum error pixelwise (\(0.05\pm 0.01\)). These small errors and high similarity scores demonstrated that the time series sensor data produced using the FNO network and k-Wave simulations were highly similar.
## 4 Discussion
There is no certainty we achieved optimal performance, and the conditions to attain such performance can only be ascertained within the researcher's efforts. The third reason for poor performance is that we had to set a very small batch size to fit the model into GPU memory. Under a small batch size, the statistics for batch normalization degrade the performance of the unstable station model. However, although ViT has been reported to perform better than some other models, it is not possible to reliably judge the classification performance, owing to the lack of consistency, as the techniques are not implemented on the same criteria, such as the number of data samples, form, preprocessing technique, and database. Moreover, the best and most accurate technique for diagnosing Alzheimer's disease has yet to be determined. Deep learning models, such as CNNs, appear promising for AD diagnosis, especially given that they can utilize transfer learning to overcome the availability limitations of many medical images [20]. The limitations of this study are, first, that it was conducted with data from the Dong-A University Hospital cohort only. According to Table 1, we can see the imbalance of the classes included in this study and the BAPL scores corresponding to the classes. We expected the model to be able to learn the final AD classification through training, as opposed to the BAPL score classification typically performed with PET images. However, the results suggest that the number of data was not large enough for the model to learn the final AD classification rather than the BAPL score classification. Not only did the HC group misclassify 100% of the two subjects in BAPL 2, but the majority of the MCI group (61 subjects in BAPL 1, 17 subjects in BAPL 2, and 35 subjects in BAPL 3) were misclassified as either AD or HC. To solve this data imbalance and lack of data, we augmented the data by rotating the images, but this only made the data more imbalanced, resulting in worse classification performance. Another limitation is that we did not train various models and compared the AD classification performance with only two models, ViT and VGG19. Currently, there are many different ViT models and CNN models for medical image classification. It is difficult to say that our study has found and compared the most suitable model among them. In the future, we plan to conduct a comparative study between the advanced ViT and CNN models by acquiring more data. In addition, we plan to apply dual PET images by adding early PET images [41], as it is considered that there are limitations in classifying HC, MCI, and AD with PET images alone.
# Current Trends in Fully Automated On-Line Analytical Techniques for Beverage Analysis
## 2 On-Line Solid-Phase Extraction (On-Line SPE)  
The most common and widespread sample preparation technique is liquid-liquid extraction (LLE), which is based in its simplest form on the division of the analyte between water and an immiscible organic solvent. However, due to the necessity to analyze complex matrices requiring more selectivity than that offered by LLE, solid-phase extraction (SPE) emerged as an alternative extraction technique. Modern SPE allows a broad field of applications and is often been the subject of detailed studies and reviews [12; 13]. In this technique, during the extraction process, an aqueous sample passes through a sorbent material (solid-phase) and the analytes are extracted with a suitable organic solvent. However, limited efficiencies can be observed caused by poor retention, principally in the care of very polar analytes, which means low recoveries during the extraction process. To overcome this drawback, other types of materials have been developed and utilized [12].  
In SPE, the analytes to be extracted are divided between a solid phase (sorbent) and a liquid phase (sample). The analytes must have a higher affinity for the solid phase than for the sample matrix. Generally, a typical SPE experiment consists of four steps: Conditioning of the sorbent phase; sample loading by passing the sample through the sorbent; washing of the column to eliminate interferences with proper solvents; and finally the elution of the analytes retained in the sorbent.  
The Nernst distribution law describes the basis of the extraction procedure. A compound is distributed between two immiscible solvents according to a constant ratio of their concentrations between the two solvents. The system constant is described as the Nernst distribution coefficient, \(K_{d}\), and is presented according to the relationship:  
\[K_{d}=\frac{C_{s}}{C_{m}}\ (T\ constant) \tag{1}\]  
where \(Cs\) and \(Cm\) are the concentrations of compounds in the solid (s) and liquid (m) phases, respectively [14].
# A Trust-Based Model for Secure Routing against RPL Attacks in Internet of Things
## 6 Discussion  
The exponential increase in the number of smart devices emphasize the need for security solutions in IoT domain. In this research, the security of RPL is focused, particularly the defense against Rank and Blackhole attacks. The trust-based approach is chosen because of its being lightweight [56], and viability of easy integration and implementation in IoT. Based on the critically chosen trust metrics, including the mobility-based metrics, a trust model is designed, and successfully integrated with RPL routing operation. The simulation study is carried out using ContikiOS/Cooja simulator for the performance evaluation of the proposed trust-based model for secure routing. The proposed SMTrust model outperforms the existing approaches. It provides security against RPL Rank and Blackhole attacks with enhanced network performance in terms of topology stability, throughput, packet loss rate, and power consumption.  
The novelty and usefulness of the proposed trust-based model, in general, are summarized as follows:  
* It provides secure communication in terms of routing among the resource-constrained nodes in IoT.
* It is suitable to be integrated into a P2P distributed network that consists of resource-constrained IoT nodes.
* It enhances secure, reliable, and trustworthy communication in IoT.
* It is a step closer to ensuring the availability and integrity of packet exchange in the network.
* It offers scalability with mobility of nodes and flexibility for various attacks detection and mitigation in RPL.  
Overall, the performance of SMTrust is significantly better as compared to default RPL objective function MRHOF and existing trust-based approaches, such as SecTrust [14], DCTM [16], and MRTS [15].
# Abstract
## 4 Use Case Scenario  
### The RegNet Platform  
RegNet is a privacy-preserving data-access and data-collaboration platform for the regulated sectors, and addresses data-privacy challenges by combining DLTs, cryptography and machine learning [20]. Participants can request and provide access to each other's data while the resulting sharing agreements are stored in a distributed ledger. RegNet seeks to provide a trusted infrastructure to enable the exchange of data in a way where no sensitive information leaves the data-holders' firewalls. To ensure both security and relevance of exchanged information, RegNet uses privacy enhancing techniques on the data accessed between participants. In addition, RegNet also implements federated learning capabilities [47] that promotes a secure collaborative way to build larger data models together with semi-trusted participants.  
RegNet's decentralized capabilities are provided by HF. HF is a permissioned distributed ledger platform targeting enterprise-grade business applications [48; 49; 50]. In addition to usual blockchain features, such as a decentralized ledger and tamper-proof data sharing, HF offers a more efficient consensus mechanism with higher throughput [51]. HF also addresses scalability and privacy issues by establishing the concept of channels. Channels allow the chosen data to be shared only among permissioned participants and thus provide a more adaptive data protection structure [52]. Areas of application include a digital vaccine passport [53], anti-counterfeit system [54], privacy-preserving in healthcare [55] and E-Voting System [56].  
Since RegNet's decentralized capabilities are based on HF, HE appears as the default endpoint to access the ledger data and provide participants with monitoring and auditing features. However, the API implemented by HE is not entirely complying with the RESTful standard and does not allow the implementation of a suitable auditing layer for the data access applications that run on RegNet. In addition, a clear need for abstraction from HE's data arose. We therefore designed and implemented the discussed abstraction layer to more accessibly provide the interfacing and aggregating functionalities needed for analytics.
###### Abstract  
Herein, a recently developed UAV/Drone approach as a new vector for the collection of airborne particulate matter is reported. In this study, airborne particle emissions from plumes generated in a holiday fireworks display were collected. A platform fabricated using a 3D printer was mounted on the drone, which allowed for particulate capture using double-sided carbon tape attached to aluminum disks. The drone platform was used to trap airborne samples from two types of plumes: high-altitude sampling (HAS), which relates to professional fireworks, and low-altitude sampling (LAS), associated with personal fireworks. Collected samples were studied using a Scanning Electron Microscope alongside Electron Dispersal X-ray Spectroscopy (EDX) for elemental composition analysis. The overall findings regarding the physical morphology reveal several key observations. Firstly, particles from professional fireworks are significantly larger and more spheroidal than those from personal fireworks. Secondly, both types of fireworks show a consistent trend in which some of the larger particles have finer particulates deposited on their surfaces. Lastly, the plumes produced by both types contain spheres that are either solid, hollow or exhibit a core-shell structure. EDX analysis revealed the presence of various types of metals within the samples. EDX analysis shows that the samples collected from the HAS and LAS contain particulates with common elements. However, the samples from the plume of professional fireworks appear to have Ba, Mg, and Fe compared to the samples from personal fireworks. These elements are known to be used in powerful fireworks to create colored displays. A proposed mechanism for particulate growth in fireworks is proposed and discussed.  
air pollution; airborne particulates; fireworks; SEM; electron dispersal X-ray spectroscopy (EDX) +
Footnote †: journal: Applied sciences  
1
# The Experiments and Stability Analysis of Hypersonic Boundary Layer Transition on a Flat Plate
## 2 Experiment Facility and Model
### Numerical Setup  
In this investigation, two-dimensional, compressible Navier-Stokes equations are solved utilizing an upwind finite-volume formulation. The transition model employed in all simulations is the \(k\)-\(\omega\) SST model, introduced by [PERSON] [29] to address certain limitations within the realm of two-equation turbulence models. The \(k\)-\(\omega\) SST model integrates the advantages of [PERSON]'s \(k\)-\(\omega\) model [30] for capturing turbulent flow near the wall and the characteristics of the k-\(\epsilon\) model in regions far from the wall. This integration is achieved through a coupling function, ensuring the conservation of compressible equations for turbulent kinetic energy and specific dissipation rate, respectively.  
\[\frac{\partial(\rho k)}{\partial t}+\frac{\partial(\rho u_{j}k)}{\partial x_{ j}}=\tau_{ij}\frac{\partial u_{j}}{\partial x_{j}}-\beta^{*}k\omega+\frac{ \partial}{\partial x_{j}}\Bigg{[}\rho(\
u+\sigma_{k}\
u_{T})\frac{\partial k }{\partial x_{j}}\Bigg{]} \tag{5}\]  
and  
\[\begin{array}{l}\frac{\partial(\rho\omega)}{\partial t}+\frac{\partial(\rho u _{j}\omega)}{\partial t}=\frac{\gamma}{\
u_{T}}\tau_{ij}\frac{\partial u_{j}} {\partial x_{j}}-\beta\omega^{2}+\frac{\partial}{\partial x_{j}}\Big{[}\rho( \
u+\sigma_{\omega}\
u_{T})\frac{\partial\omega}{\partial x_{j}}\Big{]}\\ +2(1-F_{1})\frac{\partial\
u\omega}{\omega}\frac{\partial k}{\partial x_{j}} \frac{\partial\omega}{\partial x_{j}}\frac{\partial\omega}{\partial x_{j}} \end{array} \tag{6}\]  
where \(\beta\), \(\beta^{*}\), \(\gamma\), \(\sigma_{k}\), and \(\sigma_{w2}\) are closure constant coefficients.  
The model is completed by the kinematic viscosity limitation of the following form:  
\[\
u_{T}=\frac{k}{\max\Bigl{(}\omega,\frac{\Omega\Sigma_{p}}{a_{1}}\Bigr{)}} \tag{7}\]  
where \(\Omega\) is the vorticity magnitude, a constant coefficient, and a function with asymptotic behavior. Boundary conditions on a smooth wall are as follows:  
\[k_{\omega}=0,\omega_{w}=10\frac{6\
u}{\beta_{1}y^{2}} \tag{8}\]  
with \(\beta_{1}\) being a constant coefficient.  
The numerical calculation model conditions are the same as the experimental conditions, with a length of 0.5 m and a height of 0.1 m. First, we calculate the number of grids to 200 \(\times\) 150; the height of the first layer of grid on the wall is 1 \(\times\) 10\({}^{-5}\) m, satisfying y\({}^{+}\) less than 1. The schematic diagram of the grid is shown in Figure 3. The incoming flow boundary condition is the pressure far field, the outlet is the pressure outlet boundary condition, and the upper and front areas of the plate are set with symmetric boundary conditions.  
Figure 3: Schematic diagram of flat-plate mesh division and boundary conditions.
# EEG-Based Methods for Diagnosing Color Vision Deficiency: A Comprehensive Review
## 1 Introduction
A brain-computer interface (BCI) is a technology that permits connections from one's brain to external devices. It translates brain signals, such as electroencephalography (EEG), into instructions for controlling an external device. Since BCI provides an alternative method of communication with the external world, it is primarily targeted to individuals who are unable to use conventional control and communication methods due to various disorders, such as spinal cord injury and amyotrophic lateral sclerosis [22]. As it can offer reliable information about the underlying brain processes, BCI technology can also be employed for diagnostic purposes. The diagnosis of CVD is one example of how a BCI system could be used. This review paper aims to provide a comprehensive overview of existing studies that have demonstrated the diagnosis of CVD using EEG, discussing the techniques and analyses employed. While the field of EEG-based methods for diagnosing CVD has been explored in various studies, there is currently no review paper summarizing the advancements in this area from multiple perspectives and including the most recent studies, as presented in this review. This review offers a distinct advantage over existing reviews in this field in two key aspects: firstly, it delves into a wider array of perspectives, and secondly, it encompasses a greater number of studies, particularly recent ones, setting it apart from previous reviews in the domain. Providing such a review can aid in the development of more reliable procedures in the future, as researchers can gain a comprehensive understanding of the topic from multiple perspectives, including the techniques utilized, along with their advantages and disadvantages, the features employed, the electrodes used, and ultimately, the results obtained. This comprehensive review will encapsulate the work conducted in this field. This review paper begins by introducing the key principles of color perception in the human retina to provide insight into the occurrence of CVD. Subsequently, it offers a general explanation of EEG utilization as a diagnostic tool and discusses how CVD can impact EEG readings. The review then explores previous techniques and studies used in this domain, highlighting their advantages and limitations. Following this, a brief overview of analysis techniques employed in previous studies is presented to outline the available pre-processing options in the field. Finaly, the paper concludes with a discussion on future directions and research perspectives, summarizing key gaps that can be addressed and improved upon in the future.
## 4 MIMO Performance Parameters  
In this section, we introduce the proposed MIMO antenna by computing its values for key crucial factors that define MIMO antenna systems, such as directional gain and equal-channel coupling (_DG_ and _ECC_). This section provides examples of these parameters.
## 2 LEA Detection and Tracking
### LEA Tracking with Kalman Filtering  
The Kalman filter is used to estimate the location of the LEA in the received image when the relative position between the camera and the LEA varies. In addition, the smoothing effect of the Kalman filter improves the tracking result by reducing the uncertainty of the measurement noise [18]. Filtering also helps to handle the situation in which the corners of the LEA that are momentarily missed can be detected. For example, the LEA might be misrecognized as another similar square object or be obscured by obstacles.  
We use a discrete-time Kalman filter to predict the motion of the LEA in the received image plane. To apply the Kalman filter to LEA tracking, we select the corner points of the LEA as the variables of the Kalman filter; therefore, the state vector \(\mathrm{x}_{k}\) and the measurement vector \(\mathrm{y}_{k}\) at time step \(k\) are defined as in Equation (5). Figure 7 presents a detailed overview of the discrete-time Kalman filter operation [13]. In Figure 8, we can see the four corners of the LEA, which are used as the variables of the Kalman filter.  
\[\mathrm{x}_{k}=\left[x,\ y,\ v_{x},\ v_{y}\right]\mathrm{y}_{k}=\left[x,\ y\right] \tag{5}\]
# The Use of Transfer Learning for Activity Recognition in Instances of Heterogeneous Sensing
## 1 Introduction  
Activity recognition (AR) is a research area focused on identifying human actions from a collection of sensor data. Often, solutions rely on design-time techniques that remain static after being trained once and assume that further data derives from identical sources and conditions. However, real-world applications require solutions to embrace the unpredictability of daily living and allow for exposure to unexpected changes. A practical alternative is required to address these issues, and transfer learning, if carried out successfully, can significantly improve the performance of AR data-driven modelling.  
Transfer learning is inspired by the capabilities of humans to transfer knowledge across tasks, where the knowledge from one domain can improve the learning performance or minimise the number of labelled samples required in another domain. It is of particular significance when tackling tasks with limited labelled samples. Within AR, transfer learning is an approach in which previous knowledge or a previous process addresses a new, but related, task [1, 2]. It accomplishes this by reusing the existing learned patterns on a source domain to identify the similarities within tasks, regardless of the environment or technological setting in a target domain. Hence, while conventional solutions to address AR problems rely on machine-learning approaches, the benefits of reusing existing knowledge can positively impact the training time of machine-learning algorithms.  
Transfer learning techniques have proved their wide applicability to the automatic learning and recognising of activities from scenarios where training data is already available in the source domain, enabling the transfer of knowledge to different technologies, subjects, and settings at run-time. These techniques are being used as a starting point for training classification machine-learning models, such as in a convolutional neural network (CNN) that allows classification models to reduce the training investment. In particular, researchers have employed transfer learning to extend the features extraction when training machine-learning classifiers [3]. Two of the attributes commonly investigated forCNNs are architectures [4] and fine-tuning [5], where researchers use already trained CNN architectures and extend them by incorporating new convolutional layers to fine-tune the machine-learning models. Although these strategies are common in scenarios where the feature-distribution is similar between domains, other techniques, such as teacher/learner (T/L), active learning (AL) and fusion techniques denote an alternative to address problems where the feature-distribution is different [6].
# A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems
## 2 Overview of the FRS
### Categories of FRS
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Category** & **Techniques** & **Advantages and Disadvantages** \\ \hline Content-Based Filtering & Recommends items similar to those the user has liked before & Effective at recommending niche items; limited to recommending items with similar characteristics; may not work well for users with varying interests \\ \hline \multirow{3}{*}{\begin{tabular}{p{113.8 pt}} Knowledge-Based Systems \\ \end{tabular} } & Uses knowledge about user preferences and item attributes to recommend items & Allows for more personalized recommendations; can recommend items outside of the user’s usual preferences; requires many data and expert knowledge; may not work well for users with varying interests \\ \hline \multirow{3}{*}{\begin{tabular}{p{113.8 pt}} Collaborative Filtering \\ \end{tabular} } & Recommends items based on the preferences of similar users & Can recommend items outside of the user’s usual preferences; can work well with large datasets; requires many user data to be effective; can suffer from the “cold start” problem for new users and items \\ \hline \multirow{3}{*}{\
\begin{tabular}{p{113.8 pt}} Context-Aware Recommendation \\ \end{tabular} } & Recommends items based on additional contextual information, such as time of day or location & Can provide more personalized recommendations based on specific situations; requires additional data sources and can be difficult to implement \\ \hline \multirow{3}{*}{
\begin{tabular}{p{113.8 pt}} Hybrid \\ \end{tabular} } & Combines two or more of the above techniques & Can provide more accurate recommendations by leveraging the strengths of multiple techniques; can be complex to implement and requires additional computational resources \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of categories of FRSs.