esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Reliability of Load-Sharing Redundancy  
The concept of load sharing was proposed to deal with the problem that most redundant components are treated as parallel redundancy, which is not true in reality [21]. The quantification of the system reliability of redundant components is determined based on the assumption that when one redundant component fails, the failure rates of other components do not change during the mission. However, this assumption is not correct in engineering practice. The remaining components will suffer higher failure rates due to the increased share of the load during the mission, which will reduce the system's reliability.  
Considering a two-component load-sharing system, it is reasonable to assume that the lifetime of components follows a two-parameter Weibull distribution with scale parameter (\(\lambda\)) and shape parameter (\(\gamma\)). The state transition diagram the system is depicted in Figure 7. The system reliability function (\(R_{\text{sys}}^{Is}(t)\)) can be derived as follows.  
\[R_{\text{sys}}^{Is}(t)=R_{1}(t)R_{2}(t)+Q_{1}(t_{1})R_{2}(t_{1})R_{2}^{\prime \prime}(t-t_{1})+Q_{2}(t_{2})R_{1}(t_{2})R_{1}^{\prime\prime}(t-t_{2}) \tag{16}\]  
The first term of Equation (16) is the probability that component 1 and 2 complete their mission from 0 to \(t\) successfully with \(pdf\)'s \(f_{1}(t)\) and \(f_{2}(t)\), respectively. It can be expressed as  
\[R_{1}(t)\cdot R_{2}(t)=e^{-(\lambda_{1}\cdot t)\gamma_{1}}\cdot e^{-(\lambda_ {2}\cdot t)\gamma_{2}} \tag{17}\]  
The second term of Equation (16) is the probability that component 1 fails at \(t_{1}<t\) with \(pdf\)\(f_{1}(T)\), and component 2 functions until \(t_{1}\) with \(pdf\)\(f_{2}(T)\) and then functions for the rest of the mission with \(pdf\)\(f_{2}^{\prime\prime}(T-t_{1})\). The second term of Equation (16) can be written as follows  
\[Q_{1}(t_{1})R_{2}(t_{1})R_{2}^{\prime\prime}(t-t_{1})=\int_{0}^{t}\gamma_{1} \cdot\lambda_{1}^{\gamma_{1}}\cdot t_{1}^{\gamma_{1}-1}\cdot e^{-\left\{( \lambda_{1}\cdot t_{1})\gamma_{1}+\left[\lambda_{2}^{\prime}\left(t-t_{1}+ \frac{1}{\lambda_{2}^{\prime}}e^{(\lambda_{2}\cdot t_{1})\gamma_{2}/\gamma_{2 }^{\prime}}\right)\right]^{\gamma_{2}}\right\}}dt_{1} \tag{18}\]  
The third term of Equation (16) can also be obtained in the same way, as follows:  
\[Q_{2}(t_{2})R_{1}(t_{2})R_{1}^{\prime\prime}(t-t_{2})=\int_{0}^{t}\gamma_{2} \cdot\lambda_{2}^{\gamma_{2}}\cdot t_{2}^{\gamma_{2}-1}\cdot e^{-\left\{( \lambda_{2}\cdot t_{2})\gamma_{2}+\left[\lambda_{1}^{\prime}\left(t-t_{2}+ \frac{1}{\lambda_{1}^{\prime}}e^{(\lambda_{1}\cdot t_{2})\gamma_{1}/\gamma_{1 }^{\prime}}\right)\right]^{\gamma_{1}}\right\}}dt_{2} \tag{19}\]  
The system reliability of load-sharing redundancy for a mission of duration \(t\) can be obtained by substituting the \(pdf\)'s involved into Equations (17)-(19) (\(t_{1},t_{2}\in[0,t]\)).
## 3 Experiment and Results
### Evaluation Datasets  
The performance of the proposed model was evaluated using three public datasets containing EEG signals generated by audiovisual stimuli.  
* DEAP [37]: The DEAP dataset contains EEG and peripheral signals from 32 participants (16 males and 16 females between the ages of 19 and 37 years). EEG signals were recorded while each participant watched 40 music video clips. Each participant watched each video and rated the levels of arousal, valence, dominance, and liking on a continuous scale of 1 to 9 using a self-assessment manikin (SAM). Each trial contained a 63 s EEG signal. The first 3 s of the signal is the baseline signal. EEG signals were recorded at a sampling rate of 512 Hz using 32 electrodes. In this paper, EEG data from 24 participants (12 males and 12 females) were selected for the experiment;
* MAHNOB-HCI [38]: The MAHNOB-HCI dataset includes the EEG and peripheral signals. EEG signals were collected at a sampling rate of 256 Hz from 32 electrodes while each of the 27 participants (11 males and 16 females) watched 20 selected videos. The video clip used as the stimulus had a length of approximately 34-117 s. Each participant watched each video and self-reported the levels of arousal, valence, dominance, and predictability through the SAM on a nine-point discrete scale. For the various experiments performed in this study, 22 of the 27 participants (11 male and 11 female) were selected;
* SEED [39]: The SEED dataset provides EEG and eye-movement signals from 15 participants (7 males and 8 females). The EEG signals of each participant were collected while watching 15 Chinese movie clips, each approximately four minutes long, designed to elicit positive, neutral, and negative emotions. The sampling rate of the signal collected using the 62 electrodes was 1 kHz, which was later downsampled to 200 Hz. After watching each movie clip, each participant recorded the emotional label for each video as negative (\(-1\)), neutral (0), or positive (1). The experiment was performed using EEG data from 12 of the 15 participants (6 males and 6 females) were used.
## 3 Results and Discussion
### In Silico Evaluation Results
#### 3.2.1 Docking Between GA3 Ox2 and the Ligands GA3 and FCF  
The binding affinity between GA3 Ox2 and its endogenous ligand GA3 (GA3 Ox2-GA3) was calculated at \(-9.2\) kcal/mol, with interactions involving amino acids HIS208, HIS227, PHE310, THR206, SER230, ARG337, ILE119, PRO118, and MET334. In contrast, the docking analysis between the GA3 Ox2 receptor and the ligand FCF revealed a binding energy of \(-7.8\) kcal/mol, interacting with residues ASN210, ARG295, ASP229, HIS227, ILE119, LEU224, PHE301, SER297, TYR212, and VAL236. Figure 6 shows the 3D interaction maps of the gibberell receptor (GA3 Ox2) with its endogenous ligand GA3 (a) and with FCF (b) alongside the respective 2D interaction maps (c,d).  
Figure 5: Bright field micrographs captured using transmission electron microscopy (BFTEM) for the samples Hap-H3 (**A–A3**), Hap-H3 750 (**B–B3**), Hap-FCF (**C–C3**), and their ring diffraction patterns for the samples Hap-H3 (**A4**), Hap-H3 750 (**B4**), Hap-FCF (**C4**) [40].  
These findings strongly support the potential of the gibberellin receptor to bind with FCF, as evidenced by the observed interactions. To ensure the reliability of these results, we conducted an in-depth molecular dynamics study, which confirmed the stability of the interaction over time and reinforced the robustness of our conclusions.
# Coexistence of 5G/6G and Digital Terrestrial Television Networks
## 1 Introduction  
Currently, 5G networks are being deployed worldwide, and many countries are in the preparation phase for new 6G networks. However, the implementation of 5G networks in many regions is gradual and limited [1]. In the coming years, 5G coverage will not extend to many geographical areas, particularly rural and remote locations where coverage is necessary. These areas benefit from low-frequency bands, which offer better propagation conditions. The spectrum below 1 GHz (Ultra-high frequency (UHF) 300-1000 MHz range) is optimal for 5G/6G wide network coverage and in some countries, the 700 MHz band is already deployed for such purposes. However, the 700 MHz band (694-790 MHz) is quite limited (e.g., in Europe, only 2 \(\times\) 30 MHz frequency-division duplex (FDD) spectrum is used) and more spectrum in the UHF band will be required for nationwide 5G/6G networks in the coming years. Due to the rapid development of wireless telecommunications and the increasing use of radio spectrum, there are growing issues related to the scarcity of available radio frequencies, necessitating new spectrum solutions. Spectrum sharing, where the same part of the spectrum is used for various types of communications across different radio services (e.g., mobile, fixed, satellite, or broadcasting) in the same frequency bands and geographical areas, is becoming increasingly beneficial. This issue is particularly significant for low-frequency ranges (i.e., UHF bands below 1 GHz) [2, 3], where systems benefit from excellent propagation conditions and efficient, small-sized antennas. Shared and interleaved spectrum usage in the same frequency range for network coexistence can be recognized and accepted at the regulatory level. Such radio spectrum management occurs at multiple levels: globally (regulated by the International Telecommunication Union (ITU) Radio Regulations, applicable to all ITU member states); regionally (e.g., agreements or regulations from regional organizations such as the European Conference of Postal and Telecommunications Administrations (CEPT), the Radio Spectrum Policy Group (RSPG) in Europe [4], or the European Commission (EC)) [5]; and nationally (by national regulatorsand national frequency allocation tables). Survey and guidelines for field measurements of coexistence of DTT and mobile broadband were presented in [6], and studies for SDL-LTE used in the UHF band were analyzed in [7; 8].
## 3 Results and Discussion
### Dynamic Viscosity  
The dynamic viscosity (\(\eta\)) of hair conditioners is an important determinant of cosmetic quality. It has an impact on the functional properties of cosmetics, i.e., ease of dispensing from the package or spreadability on hair. Consumers commonly assert that high viscosity is an indication of high content of active ingredients in hair conditioners. However, the dynamic viscosity of cosmetics is determined not only by the active ingredients but also, and primarily, by the presence of viscosity regulators and emulsifiers (surfactants).  
The dynamic viscosity (\(\eta\)) of the hair conditioners was found to range from 30,120 to 41,110 mPa\(\cdot\)s (Figure 4). Both the type and concentration of burdock and black radish powders were shown to affect the \(\eta\) values of the hair conditioners under study. Additionally, burdock powder incorporated into the hair conditioner formulations led to an increase in dynamic viscosity of approximately 2 to 26% compared to the reference formulation. Dynamic viscosity was found to rise in proportion to increasing concentrations of burdock powder. In this series of emulsions, the highest level of dynamic viscosity (38,066 mPa\(\cdot\)s) was recorded for the conditioner containing burdock powder at a concentration of 1%. An addition of black radish powder to the hair conditioners led to slightly higher \(\eta\) values relative to the results obtained for the formulations with burdock powder. The test results ranged between 38,600 and 41,110 mPa\(\cdot\)s. Similarly to the conditioners containing burdock powder, the higher the concentration of black radish powder, the higher the dynamic viscosity.  
The results of dynamic viscosity measurements obtained for emulsion-type hair conditioners reported in the literature encompass a wide range. Similarly to other cosmetic emulsions [50], hair conditioners are classified as pseudoplastic non-Newtonian liquids (product viscosity decreases with increasing shear rate). According to other authors [48] the dynamic viscosity of the cosmetic emulsions they studied varied from approximately 8 to 26,000 mPa\(\cdot\)s. For example, the viscosity of the cosmetic emulsions evaluated by [PERSON] et al. [51] was approximately 25,000 mPa\(\cdot\)s. The values of dynamic viscosity measured in the hair conditioners containing plant-derived powders show an order of magnitude consistent with the results reported in the literature.  
Figure 3: Turbiscan stability index (TSI) values of analyzed hair conditioners after 7 and 14 days.  
The rise in viscosity as a function of the increasing concentration of both plant powders under study can be explained by the fact that when solid particles in the form of a powder are incorporated, the viscosity of the continuous phase of the emulsion also rises (the plant powder acts as a \"filler\" for the continuous phase), thus increasing its dynamic viscosity and the stability of the cosmetic emulsion.
# Landsat Surface Product Validation Instrumentation:
## 2 Data Collected
### Thermal  
Thermal data collected via the MX-2 UAS essentially followed the same acquisition protocol as the reflective measurements. However, in addition to acquiring thermal imagery of the entire test site via parallel flight lines during morning, noon, and afternoon acquisitions, there were also opportunities for the UAS to hover over thermal targets of interest and'stare' at the targets for an extended time. The temperature of land, vegetation, and some human-made objects can change substantially with a slight change in wind speed or solar loading. Water, however, is an ideal thermal target, as it is temporally stable and has a known emissivity. These'stare' tests focused on the deployed thermal targets, specifically the warm and cold water baths, to assess the calibration and stability of the in-flight performance of the FLIR.  
Ground-based temperature sensors followed a less prescribed measurement schedule. The TidbiT water temperature sensors recorded data continuously and automatically, every 30 s, throughout the entire day in the water pools. The \(\mu\)FTIR requires several minutes of integration time to acquire spectral measurements. Thus, it also recorded temperature information almost continuously throughout each day as it was moved from target to target. However, the \(\mu\)FTIR deployment was coordinated with the UAS FLIR measurements to obtain simultaneous measurements over specific targets. The measurement protocol for the \(\mu\)FTIR is further detailed in [34].
# Distributed Edge Computing to Assist Ultra-Low-Latency VANET Applications
## 1 Introduction
MEC represents a distributed scheme from the traditional centralized scheme of cloud computing that achieves higher latency efficiency for performing computing tasks, since the distance between the end user and the MEC server is one or two communication hops [26]. The introduction of MEC servers at the edge of RAN achieves many benefits beside the reduction of round-trip latency; these benefits include the following [23; 24; 25; 26]:  
1. Achieving higher spectral efficiency;
2. Reduction of traffic congestion at the core network;
3. Reduction of round-trip latency;
4. Innovation of new services; and
5. Achieving higher system reliability. Our SDN-Lab proposed an intelligent core network for Tactile Internet in [24; 25; 26; 27], that deploys distributed SDN controllers with distributed OpenFlow switches to manage and control the whole network. The system achieves many benefits for 5G based systems, especially for high traffic density networks. Moreover, we developed a multilevel MEC system in [23; 26], for Tactile Internet and ultra-low latency 5G applications. The system deploys heterogeneous edge servers to provide the computing capabilities at the edge of a RAN, one communication hop away from the end user. The multilevel MEC system can be deployed side by side with the intelligent core networks for VANETs. The main contribution of this work is to provide a VANET system based on multilevel MEC system and SDN technology. The system achieves high latency efficiency, due to the deployment of MEC technology. Also, the proposed system solves the problem of high traffic density and reduces the congestion of the core network. Moreover, the system achieves higher reliability efficiency. The offloading algorithm for managing and controlling traffic flow from vehicles to edge servers is developed in a way to achieve higher latency efficiency. Section 2 introduces the related works that are most relevant to our proposed system and the novelty of the proposed structure compared to these systems is introduced. Section 3 provides the proposed system structure. In Section 4, the developed latency-aware offloading algorithm is introduced for the proposed structure. Section 5 provides the experimental evaluation of the proposed system and the developed algorithm. In Section 6, the realized system is introduced.
# Prediction of Students' Adaptability Using Explainable AI in Educational Machine Learning Models
## 5 Comparative Analysis of XAI Techniques and Synthesis of Findings
### Limitations  
Despite the remarkable findings and contributions of this study, it is important to acknowledge a number of limitations.  
1. The dataset included in this study was sourced from a publicly accessible website (www.kaggle.com, accessed on 18 November 2023), despite being reliable and representative of a range of student characteristics. This restricts the extent of generalizability since the dataset might not include all the subtle characteristics seen in various educational environments throughout the world. Although the dataset effectively captures various demographic and socio-economic factors, it lacks contextual elements like cultural and extracurricular influences, which are also significant for a holistic understanding of student adaptability. Despite these limitations, the dataset remains a valuable resource for exploring key factors influencing student adaptability and developing targeted educational interventions.
2. For analysis, the study mostly employed quantitative data. Qualitative data, from surveys or interviews with students, may provide deeper, more individualized insights into the variables influencing students' adaptation. This might aid in comprehending the contextual elements that are hidden by quantitative data on its own.
3. Cross-sectional data were used in the study which captures a snapshot of student adaptability at a single point in time. This method ignores periodic shifts in students' adaptability, which might offer more detailed information about how adaptability develops over time and is impacted by different kinds of interventions.
## 7 Conclusions  
In this paper, a novel hybrid computer aided diagnostic system HyCAD-OCT is introduced that effectively distinguishes a range of retinal diseases from OCT scans. The proposed HyCAD architecture integrates deep and traditional learning paradigms to present an accurate timely diagnosis. In addition, HyCAD provides an explainable diagnostic decision through automatic RoI localization using Norm-VGG16 activation maps and the extraction of human articulated features. The automatic RoI localization provides a relative advantage compared to traditional segmentation methods as it does not require expert involvement. Our HyCAD architecture proves the importance of combining automatic feature extraction and hand-crafted features in achieving higher performance metrics. Moreover, the hand-crafted features are more interpretable than the learned features by deep learning architectures models. For the employed deep learning architecture, a set of modifications are applied on the standard VGG16 architecture creating the Norm-VGG16 integrating kernel regularizer variant. Our modified Norm-VGG16 achieved better results than ResNet-50 and Kermany et al. models. HyCAD outperforms the Kermany et al. pure deep learning model in terms of accuracy and Urgent Referrals sensitivity. Similarly, our HyCAD architecture surpassed the Urgent Referrals sensitivity of [PERSON] et al. In case of binary classifications of retinal diseases vs. normal, HyCAD had a superior performance compared to both Kermany et al. and Li et al. achieving 100% accuracy, 100% sensitivity and 100% specificity for CNV and DME. For the bootstrapping experiment, HyCAD showed higher mean accuracy and Urgent referrals sensitivity when compared to Norm-VGG16 and ResNet-50. In addition, the results had lower standard deviation. Overall, a new robust diagnostic system is proposed which offers automatic RoI segmentation that can be used in assisting ophthalmologists in their decision with a range of retinal disorders. Moreover, HyCAD is a general architecture that can be trained and applied on similar problems, since no underlying specific assumptions were made that would hinder its generalization.  
Conceptualization, M.R.I., K.M.F. and S.M.Y.; methodology, M.R.I., K.M.F. and S.M.Y.; software, M.R.I.; validation, M.R.I., K.M.F. and S.M.Y.; formal analysis, M.R.I.; investigation, M.R.I.; resources, M.R.I. and K.M.F.; writing--original draft preparation, M.R.I.; writing--review and editing, K.M.F. and S.M.Y.; visualization, M.R.I.; supervision, K.M.F. and S.M.Y.; project administration, K.M.F. and S.M.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Factors Affecting Adsorption  
Figure 4 shows the influences of temperature, pH value, and electrolyte on its adsorption performance. Pb(II) adsorbing capacity was enhanced with temperature rising from 25 to 45 \({}^{\circ}\)C (Figure 4a), indicating an endothermic adsorption process. Meanwhile, Pb(II) adsorption was also promoted with pH increasing from 2.0 to 5.0 (Figure 4b), which was in agreement with the adsorption mechanism of metal-amine complexation. At lower pH level, the amine groups was protonated, and therefore the charge repulsion between MNPs and Pb(II) ions became more intensive, leading to the restraint of Pb(II) sorption. Besides, Pb(II) adsorption was restrained with addition of various electrolytes (Figure 4c), presumably due to their competitive sorption of cations. Divalent cation exhibited stronger complexing ability with amine groups, and therefore Ca\({}^{2+}\) imposed a more remarkable inhibiting effect on Pb(II) sorption.
## 6 Unbalanced PSI-CA Protocol Based on Single-Cloud Assistance
### Protocol Process  
#### 6.2.1 Preprocessing  
1. Security parameter negotiation: Each role discusses the necessary security parameters; all parties share the large prime \(q\) used in the DH cryptographic algorithm. The small health app developer and the large medical institution negotiate to generate \(r_{1}\) and the hash function \(H\).
2. The small health app developer negotiates with the large medical institution to create an obfuscated dataset \(Y^{\prime}\); this dataset is completely useless data, which means that its elements cannot belong to either the small health app developer or the large medical institution collection.
3. Large medical institution generates a private key: The large medical institution generates its own private key \(\alpha\), for use in the Diffie-Hellman encryption algorithm.
4. Data scrambling: The small health app developer and the large medical institution each scramble their own datasets \(X\) and \(Y\).
5. Small health app developer data preprocessing: The small health app developer calculates \(h_{j}=H(y_{j})\), generates \(n_{2}\) random numbers \(\beta_{j}\), and calculates \(h_{j}\times r_{1}\).