esa-sceva/satcom-chunk-collection huggingface dataset:
# Nanomaterials for Healthcare Biosensing Applications
## 5 Future Prospects  
Nanomaterials, by a combined virtue of their small size and exceptionally high surface area, exhibit interesting electromagnetic, optical, and piezoelectric properties, which hold immense potential for exploitation in the disciplines of medical diagnostics, bioimaging, and healthcare diagnostics. In addition, nanomaterials possess excellent affinity for biomolecules, facilitating the immobilisation of
## 3 Results and Discussion
### Linear Expansion Test (Swelling) (LP) Test
#### 3.2.1 Effects of M-RHA on Swelling  
Utilizing the M-RHA stabilizer resulted in a restriction in the volume change (swelling) for all gypseous soil specimens, as presented in Figures 9-14. The lowest minimum values of swelling, achieved with a 5M-5 RHA ratio of 1:1, were 0.047%, 0.04%, and 0.01% after 7, 28, and 90 days of curing, respectively, with the swelling inhibited 99.81%, 99.78%, and 99.67%, respectively. Furthermore, the 20% M-RHA-stabilizer led to a low volumetric change after 90 days of curing at 0.011%, 0.026%, and 0.026%, with the swelling suppressed to 99.5%, 98.82%, and 98.82%, respectively. This reduction in swelling can be attributed to the restriction of ettrinigite formation and the production of more CSH compounds in the absence of calcium.
# Development and Validation of a Tool for VBOI (Virtual Body Ownership Illusion) Level Assessment
## 3 Method
### Hypotheses  
The objective of this experiment was to verify whether the evaluation tool could demonstrate variations in participants' levels of Virtual Body Ownership Illusion (Virtual BOI) based on conditions such as perspective (1 PP/3 PP), interaction method (Hand Tracking/Controller), and haptic feedback (\(\text{Haptic}/\text{Non-Haptic}\)).  
[PERSON] et al. demonstrated that haptic feedback significantly enhances Embodiment scores for virtual hands [25]. [PERSON] and [PERSON] showed that participants in their experiment adapted more easily to the shape of the virtual body assigned to them when Visuo-tactile correspondence was consistent [11]. Therefore, in this study, we aim to investigate whether Visuo-tactile and Embodiment scores align with the findings regarding the presence or absence of haptic feedback as revealed in previous research.  
Previous research has revealed that stronger Virtual Body Ownership Illusion (Virtual BOI) occurs in the first-person perspective condition compared to the third-person perspective condition [26]. [PERSON] et al. found that perspective (i.e., first-person or third-person perspective) consistently influences Body Ownership Illusion (BOI) [4], and [PERSON] et al. differentiated between first-person perspective and a 90\({}^{\circ}\) rotated perspective (third-person perspective), with the first-person perspective significantly enhancing the integration of vision and touch [27].  
\begin{table}
\begin{tabular}{c} Controller \\ First-Person \\ First-Person \\ Perspective \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Virtual BOI experiment condition description.  
Figure 2: Hand Physics Lab (VR game).  
Therefore, the following hypotheses were formulated:  
1. Virtual Body Ownership Illusion (Virtual BOI) scores will vary significantly depending on the conditions (H1).
2. The Virtual Body Ownership Illusion (Virtual BOI) scores in Condition 4 (Non-Haptic) are expected to be lower than those in Control 1 (Haptic) (H2). * The Embodiment scores in Condition 4 (Non-Haptic) are expected to be lower than those in Control 1 (Haptic) (H2a). * The Visuo-tactile scores in Condition 4 (Non-Haptic) are expected to be lower than those in Control 1 (Haptic) (H2b).
3. The Virtual Body Ownership Illusion (Virtual BOI) scores in Condition 3 (3 PP) are expected to be lower than those in Control 1 (1 PP) (H3). * The Embodiment scores in Condition 3 (3 PP) are expected to be lower than those in Control 1 (1 PP) (H3a). * The Visuo-proprioceptive scores in Condition 3 (3 PP) are expected to be lower than those in Control 1 (1 PP) (H3b).
# Automated Detection of Epileptic Seizures in EEG Signals via Micro-Capsule Networks
## 2 Methods  
First, the EEG datasets and preprocessing steps are described, forming the foundation for subsequent research. Second, the micro-capsule network model is designed, its working mechanism is explained, and the core algorithm workflow of the framework is detailed. Finally, the evaluation metrics for the EEG detection algorithm are defined.
# Real-Time, Content-Based Communication Load Reduction in the Internet of Multimedia Things
## 2 Related Research and Background
### Challenges in IoT
#### 2.1.1 Overloaded Multimedia Networks  
Considering overloaded mobile networks with MMBD, the work in Reference [12] presents a hybrid-stream scheme. This scheme utilizes spatial and temporal information in video data to reduce the data load in an overloaded mobile Internet. The research proposes a coupled model consisting of classifier based on an improved Convolutional Neural Networks (CNN) algorithm and a feature-extraction mechanism. A layered structure comprising of pre-processing, classification and load-reduction layers enables optimized image/video analysis with good precision. Individual images and groups of images, that is video clips, are classified. According to the classification, frames are dropped based on an importance measure. Overall, load-reduction and QoE occurs in simulation results from the study in Reference [12]. Smart cities present a fertile application domain as they need extensive monitoring of multiple scenarios, including traffic monitoring; security checks; and disaster management, all of which generate large amounts of data. The work in Reference [2], to tackle the problem of efficient analysis of multimedia events, presents a combination of event and multimedia processing systems. Users queries are forwarded to a Multimedia Stream Processing Engine (MSPE), where queries are resolved according to what event a user has subscribed to. A feature detection model is then used to identify features suitable for recognizing objects in images. Once the classifiers detect an image event, the result is forwarded to a user in accordance with their subscription. This model has resulted in a 66.34% accuracy in identifying suitable events with a high throughput of 110 video frames per s (fps). Although increase in the number of classes required of a classifier decreases performance, by optimal selection of the number of classes, this system succeeds in optimizing the performance of multimedia applications by identifying real-time multimedia events.
# EMD-Based Noninvasive Blood Glucose Estimation from PPG Signals Using Machine Learning Algorithms
## 2 Materials and Methods
### Preprocessing  
The raw PPG signal often contains noise and baseline drift due to factors such as breathing, motion artifacts, etc. To address this, polynomial detrending is used to remove the baseline drift, which is a low-frequency trend in the signal. This method involves fitting a polynomial (in our case a 3 rd-order polynomial) to the raw data to match the slow drift, and then subtracting it to center the waveform. After detrending, high-frequency noise in PPG signal is smoothed using a 2 nd order low-pass Butterworth filter with a cutoff frequency of 8 Hz. Additionally, residual low-frequency fluctuations are eliminated with a 2 nd-order high pass filter with a cutoff frequency of 0.5 Hz.
###### Abstract  
First-principles calculations were used to investigate several inverse Ti\({}_{2}\)CoSi-based compounds. Our results indicate that Ti\({}_{2}\)CoSi could transform from a spin-gapless semiconductor to a half metal if a quarter of the Co atoms are replaced by Ti. Ti\({}_{2.25}\)Co\({}_{0.75}\)Si would keep stable half-metallic properties in a large range of lattice parameter under the effect of hydrostatic strain, and would become a gapless half metal under the effect of tetragonal distortion. Furthermore, we substituted B, Al, Ga, P, As, and Sb for Si in the Ti\({}_{2.25}\)Co\({}_{0.75}\)Si compound. Our results demonstrate that Ti\({}_{2.25}\)Co\({}_{0.75}\)Si\({}_{0.5}\)B\({}_{0.5}\), Ti\({}_{2.25}\)Co\({}_{0.75}\)Si\({}_{0.5}\)Al\({}_{0.5}\), and Ti\({}_{2.25}\)Co\({}_{0.75}\)Si\({}_{0.5}\)Ga\({}_{0.5}\) are half-metallic ferromagnetic materials, and Ti\({}_{2.25}\)Co\({}_{0.75}\)Si\({}_{0.5}\)P\({}_{0.5}\), Ti\({}_{2.25}\)Co\({}_{0.75}\)Si\({}_{0.5}\)As\({}_{0.5}\), and Ti\({}_{2.25}\)Co\({}_{0.75}\)Si\({}_{0.5}\)Sb\({}_{0.5}\) are spin-gapless semiconducting materials. The introduced impurity atoms may adjust the valence electron configuration, change the charge concentration, and shift the location of the Fermi level.  
first-principles calculation; Heusler compounds; gapless half metals; spin gapless semiconductor +
Footnote †: journal: Applied Physics Letters
## 3 Case Study--Goldeneye CCS Site
### Data
#### 3.1.1 Description of Data Set  
The simulation output consists of a 4D dataset, three spatial dimensions; latitude, longitude and depth as well as a fourth time dimension. There are in total 1736 nodes and 24 layer that make up an irregular triangulation in the latitude-longitude dimension and the resolution is refined near the leak. In the depth dimension sigma layers are used that is the resolution varies with the bathymetry. There are 1736 \(\times\) 24 = 41,664 (nodes and layers) time series for each simulation and the time series has 1345 time steps. The time between each time step is 15 min, which means the simulations last for approximately 14 days. The data has been split in two with respect to time, where the layer closest to the seafloor in the second half of the data set is used for testing and the first half of the time series is used for training and validation. The 4 simulations we received resulted in a total of 166,656 time series of which 70% of the data in the first split have been used for training and 30% validation. After labeling the data (see Section 3.1.2), the training and validation data consisted of 24.2% and 75.8% time series labeled as leak and no-leak respectively. The test data consisted of 36.2% time series labeled as leak and 69.8% time series as no-leak. See Table 1.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 2 Background and Preliminaries
### Smart-Grid-Based Cybersecurity Standards
#### 2.5.3 NIST Special Publication 800-82  
The NIST Special Publication 800-82 (NIST SP 800-82) offers security guidelines and standards for Industrial Control Systems (ICSs), including smart grid infrastructure [50]. These guidelines and standards are essential in integrating AI-enhanced security tools and aligning system monitoring with critical infrastructure resilience goals. Its framework supports asset identification and categorization, risk-based system modeling, defense-in-depth methodologies, and real-time anomaly detection [51].
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 3 Wireless Technologies
### Cellular
#### 3.2.4 Intelligent Reflecting Surface (IRS)  
Intelligent reflecting surface (IRS) is a programmable device that can be used to achieve smart and reconfigurable wireless channels for both 5G and 6G wireless communication  
Figure 14: (**a**) A classical antenna, which radiates one signal uniformly, and (**b**) multi-user MIMO antenna, which radiates multiple signals, focused on their respective receivers, reproduced with permission from [PERSON], _Massive MIMO is a reality—What is next?: Five promising research directions for antenna arrays_, published by Elsevier, 2019.  
systems [83]. It consists of a large number of passive reflecting elements, each of which is capable of inducing a controllable amplitude and phase change to the incident signal independently. This provides the flexibility of reconfiguring the signal propagation between the transmitters and receivers, which, in turn, provides a virtual LoS for bypassing obstacles. IRSs are deployed between the transmitter and receiver channel link and convert the conventional point-to-point direction channel to a channel link between the transmitter and IRS and another channel link between the IRS and receivers [84].  
Deep learning can be applied for enhancing the IRS wireless networks, such as estimating the CSI in MIMO communications. For instance, in [85] a deep denoising neural network for mmWave IRS systems was proposed, aiming to estimate the receiver-IRS channel with reduced training overhead. The results showed robustness for a 10 dB SNR. Additionally, in [86] a deep learning approach was used for phase reconfiguration, aiming to learn and utilize the local propagation environment by assuming quasi-static flat-fading channels. It aimed to find the mapping between the received pilot signals and the optimum phase configuration and downlink transmit beamforming vector by capturing important information for the phase and beamforming setting since there is a nonlinear relation between the optimal phases and the channel coefficients. The results showed robustness which makes it possible to apply in different SNR scenarios, without repetitive training.
## 5 Conclusions and Future Work  
In our study, we introduced the Distributed Time Slot Anti-Collision protocol (DTS-AC), a novel MAC layer protocol specially designed to address the challenges inherent in dense RFID networks. DTS-AC is engineered to mitigate the interference issues stemming from RRI and RTI collisions, which can significantly hinder system performance and resource utilization.  
DTS-AC incorporates a sophisticated TDMA notification system to manage RTI concerns effectively. This notification system enables efficient coordination among readers, minimizing collisions between reader transmissions and tag responses. Additionally, DTS-AC leverages elements of the FTSMAC protocol, particularly its frequency division multiple access (FDMA) scheme, to manage frequency resources and alleviate RRI collisions. By dynamically allocating time slots and frequencies, DTS-AC optimizes resource utilization, reduces contention, and enhances overall system stability.  
Our research findings underscore the superior performance of DTS-AC compared to established protocols like FTSMAC, Pulse, MCMAC, and CORA across diverse simulation scenarios. Notably, DTS-AC demonstrates significant improvements in read efficiency, the activation of readers, and collision resolution, even in densely populated RFID networks. These enhancements translate into tangible benefits such as reduced interrogation delays and minimized failed reader interrogations, thereby enhancing the reliability and efficiency of RFID systems.  
By addressing the challenges of interference and contention in dense RFID environments, DTS-AC not only advances the state of the art in RFID technology but also lays the groundwork for further advancements in MAC protocols for multi-channel RFID networks. Future research endeavors could explore additional optimization techniques and practical implementations to further enhance the performance and applicability of DTS-AC in real-world deployments.  
The authors confirm contribution to the paper as follows: study conception and design: [PERSON]; programming: [PERSON]; analysis and interpretation of results: [PERSON] and M.O.; data curation: R.M.; draft manuscript preparation: [PERSON] and M.O.; review and editing: R.M.; review of the results and confirmation of the final version of the paper: [PERSON] and M.O.; project administration: [PERSON]. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Efficient Support Vector Regression for Wideband DOA Estimation Using a Genetic Algorithm
## 5 Conclusions and Discussion  
This paper establishes an effective SVR-based framework for DOA estimation, realizing joint multiband processing of wideband signals. On the one hand, this method reduces the input scale of the SVR model by converting the parallel input of multiple frequency points into single-frequency inputs. Consequently, the input dimension remains constant regardless of the number of sub-bands, thereby leading to a reduction in the training time for the model. On the other hand, a genetic algorithm is employed to optimize the SVR model parameters, enhancing its fitting and generalization capabilities and, thereby, improving the estimation performance for wideband signals. The simulation results demonstrate that the proposed approach achieves higher estimation accuracy and better angle resolution compared to the existing typical algorithms, alongside a significant reduction in training time. In the future, we plan to incorporate multi-task learning or multi-output regression into the proposed method, allowing the SVR model to simultaneously output the angle estimations of multiple sources and further enhance the estimation accuracy, especially under low-SNR and limited snapshot conditions.  
Conceptualization, Y.Z. and G.Z.; methodology, Y.Z.; software, J.W.; validation, Y.Z. and J.W.; formal analysis, Y.Z. and J.W.; investigation, J.L. and J.X.; resources, Y.Z.; data curation, Y.Z. and J.W.; writing--original draft preparation, Y.Z. and J.W.; writing--review and editing, Y.Z. and J.W.; visualization, Y.Z., G.Z., J.W., J.L., S.D. and J.X.; supervision, Y.Z., G.Z., J.W., J.L., S.D. and J.X.; project administration, Y.Z., G.Z., J.W., J.L., S.D. and J.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
Figure 10: The variations in training time and testing time with the number of sub-bands: (**a**) training time; (**b**) testing time.
# Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects
## 5 Application of Infrared and Visible Image Fusion
### Applications in the Field of Medical Diagnosis  
Medical image fusion seeks to enhance image quality by maintaining particular features, expanding the use of images in clinical diagnostics, and assessing medical issues. Medical image fusion has increasingly proven to provide essential benefits as clinical application requirements continue to advance. Through the study of medical images, computers or clinicians are able to make the majority of medical diagnoses. Different types of medical images employ various imaging techniques and place various emphases on the description of the human body. Computed tomography [99] (CT), magnetic resonance imaging [100] (MRI), single photon emission computed tomography [101] (SPECT), positron emission tomography [102] (PET), and ultrasound [103] are examples of common medical techniques. These methods include those that concentrate on regional metabolic power and those that concentrate on organ structure. The effectiveness and precision of diagnosis will be significantly increased, while redundant information will be removed and picture quality will be raised if medical images from various modes can be merged. As seen in Figure 19, MRI pictures can better see soft tissues that are more deformed, whereas CT scans can better observe denser and less distorted tissues. The two different types of photos can then be combined using image fusion technology to deliver more precise patient information.
# Plenty of Room at the Bottom: Nanotechnology as Solution to an Old Issue in Enhanced Oil Recovery
## 2 Nanofluids
### Nanofluids and Enhanced Oil Recovery  
Nanofluids in EOR is a relatively new field of research [15; 22; 54]. Silicon nanoparticles are currently the most common type of nanoparticles used due to economic reasons and their effectiveness in rock surface modification. Polysilicon nanoparticles (PSNPs) are an odorless ultrafine powder composed mainly of SiO\({}_{2}\)[55]. Nanoparticles are generally characterized by a high-energy state of  
Figure 2: Schemes of the different core/shell nanoparticles: (**a**) spherical core/shell, (**b**) hexagonal core/shell, (**c**) multiple small core materials coated by a single shell material, (**d**) nanomatryushka material, and (**e**) movable core within a hollow shell material. Figure reproduced from Ref. [46].  
their surfaces. As such, they are applicable in EOR since they easily adsorb onto the surface of the rock formation, altering its wettability. PSNPs can be classified according to the effect they have on the wettability of the rock as: hydrophobic and lipophilic nanoparticles (HLPNs), lipophilic and hydrophilic nanoparticles (LHPNs), and neutral wet nanoparticles (NWPNs). When a colloid of PSNPs and solvent is injected in a porous media, four different phenomena will take place: adsorption, desorption, blocking, and transport. Since PSNPs can be considered as Brownian particles, different forces are responsible for the interactions between PSNPs and porous walls: the attractive potential energy of London-van der Waals, gravity, inertia, electrostatic forces between the particles, repulsion energy of electric double layers, Born repulsion, buoyancy, acid-base interaction, and hydrodynamics [56]. Since the mass and volume of nanoparticles are practically negligible, gravity and buoyancy forces are much smaller than the others. Nevertheless, due to the action of attractive forces, nanoparticles can undergo agglomeration and form larger particles/clusters of particles with significant mass, which may result in the deposition and destabilization of the suspension. If particles and porous media have opposite charges, then, depending on particles' relative position, there might be a significant gravity-opposing force. Considering only the bulk suspension, the most relevant forces affecting the particles' stability are the attractive van der Waals and repulsive electrostatic forces, described by the classical DLVO (Derjaguin, Landau, Verwey, and Overbeek) theory. When the resultant of these forces is negative, the attraction is larger than repulsion between PSNPs and porous wall, causing PSNPs to be adsorbed.