esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Background and Overview of LEACH
### LEACH Overview  
Due to their tiny size, WSNs' nodes have limited energy resources, meaning that energy-efficient routing is compulsory to maximize the network lifetime. As discussed in Section 2, several energy-efficient routing protocols have been proposed for WSNs in the literature, each of them has its advantages and limitations. However, hierarchical and cluster based routing techniques are the most prominent ones as they foster more energy efficient operations than other alternative schemes. Low Energy Adaptive Clustering Hierarchy (LEACH) is considered as one of the most important basic cluster-based routing protocols used for efficient communication in WSNs. A clustering algorithm ensures optimal energy utilization as compared with non-clustering routing algorithms [4]. LEACH is a basic energy efficient routing protocol that minimizes the power consumption in all aspects of communication, thus improving the network lifetime. In LEACH, the network is divided into a number of clusters, each cluster comprises a cluster head (CH) and a set of cluster members (CMs) as shown in Figure 1. CMs sense and aggregate data from the sensing field and transfer it to the CH. CHs are then responsible for transmitting this data to the BS. LEACH uses randomized selection of CHs to give equal chance to all participating nodes for competing in CHs selection process rather than using a pre-determined selection of CHs. In static clustering, CHs usually transmit data continuously, consume more power, and die quickly. LEACH operates in rounds; each round consists in two phases, setup phase and steady state phase. In setup phase, a CH is selected based on the generated random number \(R(N_{i})\) between zero and one. If the random number \(R(N_{i})\) is lower than the threshold function \(T(N_{i})\), a CH node is elected as shown in Equation (8) [4].  
\[T(N_{i})=\begin{cases}\frac{p}{1-p\times(rmd\frac{1}{p})}&\text{if }N_{i}\in G\\ 0&\text{Otherwise}\end{cases} \tag{8}\]  
where \(p\) denotes the probability of selecting a node as CH in \(r\) rounds, and \(G\) represents a set of nodes that have not been selected as CHs in \(1/p\) rounds. The nodes selected as CHs generate an advertisement message and broadcast it to CMs; the CMs will then join one of the clusters depending on the maximum received signal strength (RSS). The CH creates a Time Division Multiple Access (TDMA) slot for each CM for collision free intra cluster communication. In steady state phase, CHs compress, aggregate and transmit data to the BS. In LEACH, the number of transmissions is reduced by introducing the clustering. Efficient CHs selection reduces the power consumption of individual nodes, which maximizes the overall network lifetime [3].
# Emerging Trends and Management for Sjogren Syndrome-Related Dry Eye Corneal Alterations
## 4 Extragalundular Corneal Complications
### Pathophysiological Mechanisms  
**A.** A sterile corneal ulcer/melting or keratolysis typically develops in inflamed corneas in the absence of limbal vessel fluorosis [8; 9; 10; 12; 13]. Possible mechanisms involved in their pathogenesis include the following:  
* High levels of proinflammatory cytokines, reduced levels of lacrimal gland-derived factors such as the epidermal growth factor, and neurodegeneration with decreased NGFs levels may impair the integrity, proliferation, differentiation, and migration of the ocular surface epithelium, leading to punctata keratoconjunctivitis, corneal erosion, and ulceration [45; 46; 47];
* Neuroinflammation, neurodegeneration, and consequently decreased levels of nerve growth factors (NGFs) may lead to a neurotrophic keratopathy-like syndrome characterized by corneal hypo-aesthesia and reduced lachrymal reflex with decreased aqueous tear production, blinking impairment, and reduced trophism, apoptosis, and impaired regeneration of epithelial cells and stromal keratocytes, which may lead to corneal erosion/ulcer [45; 46; 47];The analysis of melted corneas from pSS patients has indeed shown the upregulation of a wide range of proinflammatory molecules, such as IL-1, IL-17, and TNF-\(\alpha\), which may increase epithelial and keratocyte apoptosis, and of the matrix metalloproteinase (MMP)-9, which is related to increased proteolytic activity and may lead to corneal stroma thinning, melting, and perforation [85; 92].  
**B.** PUK and necrotizing keratosceritis develop in severely inflamed corneas, and possible etiopathogenic mechanisms include the following:  
* autoimmune attack against connective tissue proteins: As part of the AI connective tissue diseases (CTDs), SS represents a multisystem AI disorder affecting the connective tissue proteins [5]. Both connective and sclera-corneal tissues contain collagen and proteoglycans that may become the target of a cell-mediated and antibody-mediated autoimmune attack inducing the destruction of the peripheral corneal stroma via matrix metalloproteinases activation [88].  
The limbal vessels' deposition of the autoimmune complex may result in an inflammatory cascade involving several proinflammatory cytokines and the recruitment of neutrophils and macrophages, which release collagenases and other proteases, causing rapid keratolysis [88].
# Accurate Characterization of the Properties of the Rare-Earth-Doped Crystal for Laser Cooling
## 2 LITMoS Test Theory
Two zero-crossing wavelengths can be observed throughout the crystal temperature change. The external quantum efficiency \(\eta_{ext}\) is determined by the ratio of the average fluorescence wavelength \(\lambda_{f}\) to the first zero-crossing wavelength \(\lambda_{cross1}\)[46]. The second crossing wavelength \(\lambda_{cross2}\) helps solve the background absorption coefficient \(\alpha_{b}\) on the basis of Equation (1) with the measured parameters of \(\eta_{ext}\), \(\lambda_{f}(T)\) and \(\alpha_{r}(\lambda,\ T)\).
## 4 Local Updates to Handle Mobility of Sensors for Geocasting
### Removing [PERSON]'s Convex Hull from Its Previous Parent Node  
If the parent node of a MN does not receive another beacon packet from its mobile child after the beacon interval defined in the previous MN_BEACON packet, it concludes that it is not in the communication range of its mobile child anymore. Therefore, the convex hull of the mobile child should be removed from the convex hull of the previous parent of MN as illustrated in Figure 5 at time t12. The timeout mechanism for removing coordinates from local coverage areas is executed here to remove [PERSON]'s convex hull from the convex hull of its previous parent. It is important to point out that the timeout interval should not be shorter than the beaconing interval (_i.e._, MN_BEACON_INTERVAL) to ensure that [PERSON]'s convex hull is not removed before it is reinforced. If the aggregated local convex hull of the previous parent of [PERSON] is changed after the removal of [PERSON]'s convex hull, the previous parent of [PERSON] also sends its new convex hull to its own parent node.
## 3 Results
### Segmentation Tasks
#### 3.1.6 Machine Learning  
Machine learning is a type of artificial intelligence technique that is based on the extraction of hand-crafted features which are then fed into a classifier. This method is more commonly used for classification tasks and will be described in more detail in Section 3.2.1, but it can also be employed for segmentation tasks. In this case, the features that are extracted from regions of interest (ROIs) of the image are fed into a classifier to determine whether the current ROI belongs to the object of interest (or to which of the objects of interest they belong in the case of multi-object segmentation) or to the background. A machine learning method for a segmentation task was found in only one of the analyzed articles and was focused on the choriocapillaris segmentation [77]. The method was based on the extraction of features from the structural OCT images and the inner retinal and choroidal angiograms. In particular, the features included the standard deviation and directional Gabor filters at multiple scales which were then fed into a random forest classifier. This technique showed a final Jaccard index equal to 0.81 \(\pm\) 0.12.
# An Efficient and Fast Hybrid GWO-JAYA Algorithm for Design Optimization
###### Abstract  
Metaheuristic algorithms (MHAs) are widely used in engineering applications in view of their global optimization capability. Researchers continuously develop new MHAs trying to improve the computational efficiency of optimization search. However, most of the newly proposed algorithms rapidly lost their attractiveness right after their release. In the present study, two classical and powerful MHAs, namely the grey wolf optimizer (GWO) and the JAYA algorithm, which still attract the attention of optimization experts, were combined into a new hybrid algorithm called FHGWJA (Fast Hybrid Grey Wolf JAYA). FHGWJA utilized elitist strategies and repairing schemes to generate high-quality new trial solutions that may always improve the current best record or at least the old population. The proposed FHGWJA algorithm was successfully tested in seven engineering optimization problems formulated in the fields of robotics, hydraulics, and mechanical and civil engineering. Design examples included up to 29 optimization variables and 1200 nonlinear constraints. The optimization results proved that FHGWJA always was superior or very competitive with the other state-of-the-art MHAs including other GWO and JAYA variants. In fact, FHGWJA always converged to the global optimum and very often achieved 0 or nearly 0 standard deviation, with all optimization runs practically converging to the target design. Furthermore, FHGWJA always ranked 1 st or 2 nd in terms of average computational speed, and its fastest optimization runs were better or highly competitive with those of the best MHA taken for comparison.  
metaheuristic optimization algorithms; fast hybrid optimization algorithms; GWO; JAYA; elitist strategies; engineering problems +
Footnote †: journal: Applied Sciences
# A Gamified AI-Driven System for Depression Monitoring and Management
## 2 Background and Related Works
### Environmental Context: Temperature and Mood  
Environmental conditions, especially temperature, play a substantial role in shaping emotional states. Elevated temperatures have been linked to psychological distress, irritability, and reduced cognitive performance due to their physiological impact [23; 24]. Conversely, moderate temperatures have been associated with greater emotional stability and improved mood regulation [25].  
Seasonal changes, including variations in sunlight and temperature, have also been implicated in depressive conditions such as seasonal affective disorder (SAD), which tends to peak in winter months [26]. Despite this, many digital mental health systems do not consider the environmental context when assessing or responding to user states. Integrating environmental data streams such as weather or ambient light may enable more context-sensitive interventions and help explain periodic mood shifts [27]. This presents a largely untapped opportunity to enhance the timing and relevance of mental health support, particularly for users sensitive to seasonal or climatic factors.  
Complementing these findings, wearable-based sensing has shown promise in capturing environmental and physiological data relevant to mental well-being. For instance, [PERSON] et al. [28] reported that sleep quality indicators captured by the Oura Ring, such as duration, heart rate variability (HRV), and respiratory rate, correlated with perceived stress. [PERSON] et al. [29] developed a deep learning-enabled smart garment that identified sleep stages with 98.6% accuracy, while [PERSON] and [PERSON] [30] demonstrated successful clinical applications of wearable pulse-oximetry for home-based mental health assessment. These advances support our inclusion of ambient and sleep-related sensing for enhanced mood-state prediction.
# Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information
## 3 Preliminaries
### Angle of Arrival Measurement
However, we found that our self-adaptive MUSIC algorithm in Section 5 can accurately detect the client's AoA when we eliminate the phase offset and eliminate the NLoS path, which is shown in Figure 1(b). At this point, AoA can be accurately measured, and it can solve most, but not all of the issues in the attack model. Based on this, we propose a Sybil attack detection system.
# An Insight to the Outage Performance of Multi-Hop Mixed RF/FSO/IWOC System
## 6 Conclusions  
In this paper, a UAV-based relay-assisted hybrid mixed RF/FSO/UWOC system has been proposed to maintain communication between two distant nodes, one above the terrestrial building and the other underwater. The analytical exact closed-form expressions for the outage probability of the system have been obtained using the CDF of the end-to-end SNR of the system. The outage performance of the system has also been analyzed at the high-SNR regime to gain insight into the system's performance. The derived theoretical expressions are verified using the simulation results, showing the effect of various system parameters such as atmospheric loss, pointing error, atmospheric turbulence, and link interruption due to angle of arrival and underwater turbulent parameters of the proposed system model's performance.  
Conceptualization, H.H.A. and N.K.S.; methodology, H.H.A. and R.M.A.Q.; software, A.M.M. and M.A.M.; validation, H.K. and P.S.; formal analysis, M.A. and A.V.; investigation, M.A.M.; resources, S.B. and R.M.A.Q.; data curation, H.K. and M.A.M.; writing--original draft preparation, A.M.M., A.V. and M.A.; writing--review and editing, H.K., P.K. and P.S.; visualization, S.B. and P.K.; supervision, N.K.S. and A.V.; project administration, M.A. and H.K.; funding acquisition, H.H.A. and N.K.S. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through the Large Group Research Project under grant number RGP2/39/44.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 7: Outage probability of the system at various turbulent conditions.
# Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning
###### Abstract  
Privacy protection has been an important concern with the great success of machine learning. In this paper, it proposes a multi-party privacy preserving machine learning framework, named PFMLP, based on partially homomorphic encryption and federated learning. The core idea is all learning parties just transmitting the encrypted gradients by homomorphic encryption. From experiments, the model trained by PFMLP has almost the same accuracy, and the deviation is less than 1%. Considering the computational overhead of homomorphic encryption, we use an improved Paillier algorithm which can speed up the training by 25-28%. Moreover, comparisons on encryption key length, the learning network structure, number of learning clients, etc. are also discussed in detail in the paper.  
multi-party machine learning; privacy preserving machine learning; homomorphic encryption +
Footnote †: journal:  
0000-0001-12-394-3010]G. G. G. G. G. G. G. G. G. G. G. G. G. G. G  
able to identify the data being operated in the whole process of homomorphic operation, the security of privacy data can be guaranteed.  
The multi-party privacy protected machine learning based on homomorphic encryption proposed in the paper has a wide range of scenarios in practical applications. In addition, the main contributions of the work are as follows:  
* It provides a multi-party privacy protected machine learning framework that combines homomorphic encryption and federated learning to achieve the protection of data and model security during model training. In addition, the proposed framework can maintain the privacy data security when multiple parties learn together.
* It verifies that the model trained by our proposed algorithm has a similar accuracy rate as the model trained by traditional methods. From experiments on the MNIST and metal fatigue datasets, the accuracy deviation does not exceed 1%.
* About the time overhead of homomorphic encryption, it analyzes the impact of different key lengths and network structures, and finds that, as the key length increases or the structure becomes more complicated, the time overhead increases. Trade-off between performance and security level should pay more attention.  
The organization of the rest of the paper is as follows. The related work is summarized briefly in Section 2. In Section 3, the federated network algorithm and Paillier federated network algorithm are discussed in more detail from the point of security, interaction, and network structure. The experimental results are presented in Sections 4 and 5 summarizes the whole paper.
## 2 Materials and Methods
### Characterization of FA  
The distribution of FA particles is shown in Figure 1; it can be observed that 92% of the particles in sample FA-3 are smaller than 45 \(\upmu\)m, while FA-1 and FA-2 have a higher percentage of particles with a size greater than 45 \(\upmu\)m with 55% and 60%, respectively. It can also be observed that FA-1 contains particle sizes greater than 100 \(\upmu\)m (30%). The specific surface area obtained by Brunauer-Emmett-Teller method (BET) of FA-1, FA-2, and FA-3 is shown in Table 2.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Composition** & **SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **CaO** & **MgO** & **K\({}_{2}\)O** & **Na\({}_{2}\)O** & **SO\({}_{3}\)** & **TiO\({}_{2}\)** & **LOI \({}^{*}\)** \\ \hline \% by weight & 56.9 & 26.24 & 5.56 & 4.25 & 0.72 & 1.21 & 0.346 & 0.71 & 1.21 & 2.47 \\ \hline \multicolumn{10}{c}{\({}^{*}\) Loss on ignition.} \\ \end{tabular}
\end{table}
Table 1: Chemical composition of fly ash.  
The identification of phases obtained by XRD show that the main compounds in FA-1, FA-2, and FA-3 are the mullite (Al\({}_{6}\)Si\({}_{2}\)O\({}_{13}\)) and quartz (SiO\({}_{2}\)), respectively (Figure 2a). Shown in the diffractograms are their primary peaks at 16.3 and 26.7\({}^{\circ}\) 20, in addition to an amorphous phase between 17\({}^{\circ}\) and 30\({}^{\circ}\) of 20, which is characteristic of this type of material [33]. The XRD patterns indicated no apparent change in the mineralogy and peak intensities in the milled samples [28].  
In Figure 2b the FT-IR spectra of FA-1, FA-2, and FA-3 are shown, in which no band shifts or significant changes of FA-1 are observed respect to FA-2 and FA-3, presenting bands at 560, 792 and 1092 cm\({}^{-1}\) attributed to symmetric stretching (Al-O-Si), symmetric stretching vibrations (Si-O-Si), and asymmetric stretching vibrations (Si-O-Si and Al-O-Si), respectively. Moreover, the bands in the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sample** & **Specific Surface Area (m\({}^{2}\)/kg)** \\ \hline FA-1 & 975 \\ FA-2 & 1107 \\ FA-3 & 1371 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specific surface area of FA-1, FA-2, and FA-3.  
Figure 1: Particle size distribution of FA-1, FA-2, and FA-3.  
Figure 2: Analysis of FA-1, FA-2, and FA-3 by: (**a**) X-ray diffraction (Q: quartz, M: mullite); (**b**) FT-IR spectroscopy.  
regions of 1630 and 3437 cm\({}^{-1}\) are attributable to stretching and deformation vibrations of O-H and H-O-H groups [33; 34]. For this, only FA-1 was chosen as a reference for comparison with the FT-IR spectra of the different geopolymers (Figure 4 The transmittance spectra of FA-1 and the different geopolymers over the range of 1350-850 cm\({}^{-1}\) were subjected to a deconvolution analysis using the Gaussian peak shape method).
## 3 Results
### Effectiveness Outcomes
#### 3.2.2 Transverse Arch Dimensional Changes
The only detectable difference between the two groups was in mandibular intermal width, where conventional appliances showed a minor expansive tendency compared to PSLB appliances. Comparing PSLB with both ASLB and CB, the results of the studies are also similar regarding the development of dental arches. According to a multicenter, randomized controlled research, there is no discernible difference between CBs and ASLBs or PSLBs in the permanent dentition with regard to changes in molar buccal inclination during alignment or arch transversal dimension ([PERSON] et al., 2013) [46]. There were no variations in the maxillary arch dimensional changes between ASLBs, PSLBs, and CBs, according to an analysis of the inclination and dimension changes of the maxillary arch using these orthodontic appliances. However, the maxillary intercanine, interpremolar, and intermal widths were significantly greater after treatment in each bracket group ([PERSON] et al., 2016) [36]. In a study published in 2022, [PERSON] et al. [38] conducted a randomized clinical trial to compare the dimensions of dental and basal arches in PSLBs and ASLBs with conventional appliances. The study again revealed that there were no significant differences in maxillary intermal width, basal arch dimensions, depth of arches, buccolingual inclination, and alignment duration among ASLBs, PSLBs, and conventional brackets. However, the study did find a significant difference in the width of the dental arch in the canine and premolar regions. Self-ligating passive or active brackets resulted in increased dental arch width compared to conventional brackets. Only one study suggested that the choice of PSLB can have an impact on transverse maxillary arch width changes in patients undergoing premolar extractions. The objective of the randomized clinical trial, as reported in the article by [PERSON] et al. (2019) [47], was to compare the changes in transverse maxillary arch dimensions in first premolar extraction patients treated using ASLBs and PSLBs versus CBs. The study discovered a substantial difference between SLBs and the traditional bracket method in the dimensions of the maxillary arch width. The passive self-ligating bracket SmartClip, in particular, demonstrated the greatest increase in intermal width.
## 2 Results and Discussion
### Association between Sensory Analysis and Volatile Organic Compounds  
Orthogonal partial least-squares discriminant analysis (OPLS-DA) was performed to evaluate the overall association between the aroma-active VOCs and sensory descriptors in experimental pasta (Figure 4). Pasta C was located on the right side of the plot and was associated with sensory descriptors (elasticity, hardness, creamy colour, flour odour, flour and sweet taste) and three VOCs (hexenal, 2-pentylfuran and (_E,E_)-2,4-nonadien). Pasta B2.5 was located in the upper-left quadrant and correlated only with alcohols and ketones (2-octanone, (_Z_)-2-penten-1-ol, acetone, 1-penten-3-ol, 1-nonanol and 4-methyl-2-hexanone). Most VOCs, as well as several sensory descriptors (green colour, chewiness, adhesiveness, aftertaste, cabbage odour and cabbage taste), were associated with pasta B5, which was located in the lower-left quadrant.  
Cabbage flavour detected in pasta B5 could be attributed to the presence of dimethyl sulphide, which was reported to have a high flavour dilution (FD), suggesting that even a small concentration of this compounds contributes to its overall aroma. Because panellists detected a difference in the intensity of cabbage-like aroma between pasta B2.5 and B5, it can be assumed that other sulphur compounds, which usually have relatively low odour thresholds, could be present below the limit of detection [41]. The absence of high-molecular-weight sulphides, such as dimethyl, tri- and tetrasulphides, may be related to the fact that the SPME technique, as an absorption method, is more suitable for the extraction of low-molecular-weight compounds.