esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The use of ML algorithms in optical communication systems offers the benefit of delivering rapid and precise performance metrics, which can be integrated into feedback control mechanisms to dynamically adjust system parameters such as filter settings, electronic dispersion compensation and power adjustments. ML algorithms can infer the system's operating condition, enabling autonomous decisions such as rerouting data across alternative network paths or initiating retransmission without requiring human intervention. Additionally, traditional ML algorithms for eye diagram analysis have undergone a major shift with the emergence of deep learning (DL) techniques. Unlike conventional ML algorithms, which often lack the capability to support efficient end-to-end learning, deep learning models offer significantly enhanced learning capacity. Deep Neural Networks (DNNs) characterized by multiple nonlinear layers and a large number of interconnected neurons utilizing activation functions form the backbone allow this advancement. Various deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Deep Reinforcement Learning (DRL) have demonstrated remarkable success across numerous domains including image processing, natural language understanding, and autonomous systems [11]. Future optical networks are expected to be highly adaptive and support various modulation formats to accommodate increasing data demands. However, their heterogeneous nature introduces diverse transmission impairments, making real-time Optical Performance Monitoring (OPM) metrics like the Bit Error Rate (BER), Quality Factor (Q-factor), and transmission distance an essential requirement at intermediate nodes for reliable signal quality assessment [12].
Other experiments have demonstrated that alumina/YSZ ceramic coatings proposed for metal elements of gas turbines operating at high temperatures exhibit excellent corrosion resistance in 0.01 M K\({}_{3}\)Fe(CN)\({}_{6}\)/K\({}_{4}\)Fe(CN)\({}_{6}\) aqueous solutions [15]. The formation of double layers of alumina and YSZ, also obtained by APS, has been demonstrated to enhance corrosion resistance in CMAS, even when subjected to prolonged exposure [1]. The corrosion behaviour of plasma spray-coated austenitic steel substrates with different ceramic layers was investigated by [PERSON] in a 0.5 M H\({}_{2}\)SO\({}_{4}\) solution. Metal substrate samples with Al\({}_{2}\)O\({}_{3}\), WC-12% Co and WC-12% Co/Al\({}_{2}\)O\({}_{3}\) multilayer deposits were tested [16]. The corrosion resistance of the substrate is influenced by the type of surface coating material [8]. In contrast, oxide ceramic coatings exhibited fewer defects and superior corrosion resistance compared to carbides (WC-12% Co) [17]. Additionally, ceramic materials based on alumina or zirconia have demonstrated potential in the medical field [16]. The use of ZrO\({}_{2}\)-CaO layers, comprising 95% ZrO\({}_{2}\) and 5% CaO, as a ceramic biomaterial has been demonstrated to facilitate osteeconductivity (the formation and growth of new bone material around the implant) and provide effective protection in biological electrolytic environments [18]. ZrO\({}_{2}\)-CaO powder ceramic layers were manufactured via plasma jet spraying using a Sulzer Metco 9 MCE spray rig. The electrochemical impedance experiments proved to be an effective method for studying the strength of ZrO\({}_{2}\)-CaO layers deposited on Mg-Ca alloy during exposure in Ringer's solution. The impedance results demonstrated notable differences that could be attributed to variations in immersion time, porosity and coating thickness. A thicker layer with enhanced compactness and density was observed to exhibit superior electrocorrosion resistance. The porous nature of the layers produced by spraying resulted in the formation of some connection paths between the corrosive environment and the base metal substrate over time [19]. In this study, the protective efficacy of ceramic coatings comprising alumina and zirconia stabilised with yttria (12.5, 25 and 37.5% YSZ powder, as reported in the study by [PERSON] et al.) against a metal substrate (typically steel) in an acid rain electrolyte solution was investigated. No results have been reported on this topic.
###### Abstract  
Background: Fluoride is vital in dentistry for caries prevention, enhancing remineralization, and inhibiting bacteria. Incorporating fluoride into restorative materials like glass-ionomer cements, componmers, and giomers has significantly increased fluoride availability in the oral cavity. This review assesses how surface coatings influence fluoride release from various dental restorative materials. Methods: In December 2023, we conducted electronic searches in PubMed, Scopus, and Web of Science (WoS) databases. In the Scopus database, the results were refined to titles, abstracts, and keywords, while in PubMed, they were narrowed down to titles and abstracts. In WoS, the results were refined only to abstracts. The search criteria were based on the terms fluoride AND release AND (coating OR glaze OR layer OR film OR varnish) AND (composite OR glass OR component), following PRISMA guidelines and the PICO framework. Twenty-three studies were rigorously selected and analyzed for fluoride release from coated versus uncoated materials. Results: Surface coatings typically reduce the rate of fluoride release. Glass-ionomer cements had the highest release, followed by giomers and compomers. The initial release was greater in uncoated materials but stabilized over time, influenced by variables like artificial saliva and deionized water. Conclusions: Surface coatings generally decrease fluoride release rates from dental materials. Although initial rates are high, contributing to caries prevention, more standardized research is needed to better understand the impact of coatings and optimize materials for maximum preventive benefits.  
composite; compomer; film; glass ionomer; glaze; layer; varnish +
Footnote †: journal: Applied Sciences
# Development of Radio-Frequency Sensor Wake-Up with Unmanned Aerial Vehicles as an Aerial Gateway
###### Abstract  
The advent of autonomous navigation, positioning, and general robotics technologies has enabled the improvement of small to miniature-sized unmanned aerial vehicles (UAVs, or 'drones') and their wide uses in engineering practice. Recent research endeavors further envision a systematic integration of aerial drones and traditional contact-based or ground-based sensors, leading to an aerial-ground wireless sensor network (AG-WSN), in which the UAV serves as both a gateway besides and a remote sensing platform. This paper serves two goals. First, we will review the recent development in architecture, design, and algorithms related to UAVs as a gateway and particularly illustrate its nature in realizing an opportunistic sensing network. Second, recognizing the opportunistic sensing need, we further aim to focus on achieving energy efficiency through developing an active radio frequency (RF)-based wake-up mechanism for aerial-ground data transmission. To prove the effectiveness of energy efficiency, several sensor wake-up solutions are physically implemented and evaluated. The results show that the RF-based wake-up mechanism can potentially save more than 98.4% of the energy that the traditional duty-cycle method would otherwise consume, and 96.8% if an infrared-receiver method is used.  
unmanned aerial vehicle; aerial-ground network; energy efficiency; sensor activation; radio frequency +
Footnote †: journal: Sensors
## 2 Materials and Methods
### Graph Construction  
In the application of graph convolutional networks, constructing the graph is a crucial step [21]. During the graph construction process, the mRNA sequence is transformed into a directed graph. Given an mRNA sequence:  
\[mRNA=R_{1},\,R_{2},\,R_{3},\,\ldots,\,R_{L-1},\,R_{L} \tag{1}\]  
where \(L\) is the length of the sequence, \(Ri\) represents one of the four nucleotide bases (A, C, G, U) at position i of the mRNA sequence. To extract meaningful features from mRNA sequences, we employed k-mer representations, where each sequence is divided into overlapping substrings of length k. For example, a sequence \"AUGCUG\" with k = 3 is represented as [\"AUG\", \"UGC\", \"GCU\", \"CUG\"]. By calculating the frequency of each k-mer across the sequence, we generated feature vectors that capture the sequence's local compositional patterns. This method provides a computationally efficient way to represent sequence data while retaining biologically relevant information. Building upon this approach, a fixed window of length k is chosen, which slides along the mRNA sequence, extracting subsequences of length k each time, known as k-mer fragments. Therefore, the set of k-mers (using 4-mer as an example here) is \(\{R_{1}R_{2}R_{3}R_{4},\,R_{2}R_{3}R_{4}R_{5},\,\ldots,\,R_{L-3}R_{L-2}R_{L-1} R_{L}\}\). Next, using these k-mer fragments, we construct a graph where each k-mer fragment corresponds to a node in the graph. Additionally, if two adjacent k-mer fragments in the sequence have matching prefixes and suffixes, a directed edge is added between these two nodes to represent the connectivity of k-mer fragments in the sequence. Ultimately, the original mRNA sequence is transformed into a de Bruijn graph, where nodes represent k-mer fragments and directed edges reveal the connections between these fragments. Then,  
Figure 2: The framework of CSpredR consists of three parts: feature encoding, model, and prediction. First, the sequences are converted into de Bruijn graphs, and then two encoding methods are used to perform feature encoding for the graph nodes. In the model part, the graph features are separately input into CNN and Bi-LSTM to extract sequence features. The extracted sequence features are then fused and concatenated to serve as the input for the multi-head attention mechanism for further processing. Finally, multi-label classification predicting mRNA subcellular localization is achieved through an MLP combined with the sigmoid activation function.  
we assign a weight to each directed edge. Following the approach used in GraphLncLoc [22], the weight of the k-th edge is determined by the frequency of the (k + 1)-mer, primarily influenced by the two nodes composing this edge. To mitigate the impact of absolute differences in edge frequencies, we normalize the edge weights in the graph. Formally, \(e_{ji}\) denotes the frequency weight of the edge from node \(j\) to node \(i\), and \(N(i)\) represents the set of neighboring nodes of node \(i\). We normalize the frequency weights using the following formula:  
\[Wnorm=\frac{e_{ji}}{\sqrt{\sum_{q\in N(j)}e_{ji}\sum_{q\in N(i)}e_{qi}}} \tag{2}\]
# The Active Segmentation Platform for Microscopic Image Classification and Segmentation
## 4 Materials and Methods
### Image Segmentation Task  
The typical use of the segmentation functionality consists of 4 steps as follows: the first step is to provide defined samples of each class in different colors e.g., nucleous, background etc. as shown in Figure 2. The samples can be outlined using rectangular, polygon or composite regions of interest ROIs in ImageJ. Secondly, an array of geometric features at different scales are computed using the filters listed in Table 3 and a feature vector is generated for each pixel. In the third step, most discriminative features are selected using some useful heuristic-- i.e., for example by Correlation-based Feature Selection (CFS) [44], which uses greedy hill climbing and best first search to explore the feature space. Various other feature selection techniques i.e., Principal Component Analysis (PCA), rank-based methods or others could also be used. The AS/IJ platform supports 2 feature selection algorithms--CFS and PCA. Next, the vectors are classified. For the purposes of the paper, in order to allow for comparisons with other platforms, the random forest classifier [45] with 100 default trees was used to train the model. The reasons behind using random forest as the default classifier for segmentation are: (1) it allowed us to compare our results with Ilastik [8]; (2) it has a smaller number of parameters and scales matching well with the size of the data set. Once the training is over, AS/IJ produces a class probability map, which is then thresholded and presented for user inspection. The corresponding mask is overlaid on the original image as shown in Figure 2.  
For the purposes of comparison we have used the same filters as implemented in Ilastik. For the segmentation task, we have used two 2D topology-based segmentation metrics i.e., \(V^{rand}\) and \(V^{info}\) proposed by the challenge organizers themselves (see next section) [39]. \(V^{rand}\) and \(V^{info}\) are the normalized version of Rand error and information error after thinning the neurites border [46]. To compute the metrics we have performed 10 fold cross-validation using a random sample of 25 images for training and 5 for testing.
## 1 Introduction
### Literature Review of the Studies on Fatigue in ATCOs
It reduces the probability of reoccurrence of fatigue through management regulations and fatigue assessment, which is used to identify fatigue in time during the work shift. The influencing fatigue factors mentioned above are the primary basis for developing countermeasures. The ICAO [12] included the factors influencing fatigue in the concept of Fatigue Risk Management (FRM) and provided suggestions to reduce fatigue. The fatigue and workload measurement of ATCOs has been studied extensively. The workload of ATCOs is usually assessed with the NASA Task Load Index (NASA-TLX) [32], which belongs to subjective measurement. Objective methods such as using a complexity network [33] or conformalized dynamical graph learning [34] have also been validated. The most common method of fatigue assessment is self-reports based on questionnaires [4; 35]. ATCOs report their subjective fatigue levels according to a scale, such as the Karolinska Sleepiness Scale (KSS) [36] and the Stanford Sleepiness Scale (SSS) [37]. Validated tools to measure fatigue also include the Brief Fatigue Inventory [38], the Chalder Fatigue Scale [39], and the Fatigue Severity Scale [40], which are usually used to evaluate the severity of fatigue in fatiguing illnesses. The method is implemented easily and is valuable for summarizing fatigue symptoms and identifying fatigue. However, subjective fatigue may be imprecise due to different motivations and personal factors [41]. Several objective indicators, such as the electroencephalogram [42], eye movements [43], facial features [44], and voice [45], have been used to determine fatigue levels. In summary, occupational fatigue has been investigated extensively in different aspects, and the results have provided guidance for the management and prevention of fatigue.
## 2 Material and Method
### Aroma Compounds Extraction and Analysis by GC-MS  
Extraction and analysis of aromatic compounds were performed as described in [25] with slight modifications. A total of 50 g berries were ground in liquid nitrogen, centrifuged and filtered to acquire a clear juice. Grape juice (6 mL) was transferred to a 20 mL glass vial (Agilent Technologies, Santa Clara, USA) with 1.5 g NaCl. 2-octanol added into pulp juice as an internal standard to quantify aroma compound. The sample vial was equilibrated at 50 \({}^{\circ}\)C for 10 min. Then, solid phase microextraction fiber (SPME, 50/30 \(\upmu\)m DVB/CAR/PDMS, Supelco, Bellefonte, USA) was used to extract volatile aroma compounds at 50 \({}^{\circ}\)C for 30 min. The fiber was then immediately inserted into the gas chromatograph (Agilent 8890 GC, Agilent Technologies) injection port for desorption at 260 \({}^{\circ}\)C for 3 min in the splitless mode and equipped with a 5977 mass-selective detector (Agilent Technologies). The volatile aroma compounds were separated in a HP-Innowax column (30 mm \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m; Agilent Technologies). The temperature program was as follows: 40 \({}^{\circ}\)C for 5 min, increased to 240 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min and then increased to 260 \({}^{\circ}\)C at 20 \({}^{\circ}\)C/min before being held for 5 min. The flow rate of helium as a carrier gas was 1 mL/min. The mass spectrometry (MS) transfer line and ionization source temperature were 260 \({}^{\circ}\)C and 230 \({}^{\circ}\)C, respectively. Electron impact mass spectrometric data from 20-400 m/z were collected at 70 eV ionization voltages.  
The volatiles aroma compounds were identified via comparing their mass spectral with those from the NIST14 mass spectral library. The concentrations of volatile aroma compounds were estimated using the following equation:  
\[C_{SC}=\frac{A_{SC}/A_{IS}\times C_{IS}\times V_{IS}}{V_{S}}\]where \(A_{SC}\) is the peak area of a specific compound; \(A_{IS}\) is the peak area of an internal standard; \(C_{SC}\) is the concentration of a specific compound; \(C_{IS}\) is the concentration of the internal standard; \(V_{S}\) is the volume of samples; \(V_{IS}\) is the volume of an internal standard.
# Dynamic Network Formation for FSO Satellite Communication
## 1 Introduction  
Optimizing satellite communications is an important field in the new-space industry. Currently, most such communication is achieved by high-power radio frequency (RF) communication. In this research, we consider an optical laser-based communication network for a swarm of low-Earth-orbit (LEO) nano-satellites. The main motivation of creating such a network is to provide global internet coverage, i.e., to be able to communicate between any two points on Earth, especially in regions where terrestrial internet is rare or simply not available.  
A global communication network requires two main types of communication:  
* Inter-satellite or space-to-space communication, which enables the deployment of a space network comprised of LEO nano-satellites.
* Ground-space communication, which enables a link connecting between a ground station and a satellite; see Figure 1.  
The focus of this paper is on space-to-space communication and particularly on the formation of an inter-satellite space network in view of two principal characteristics that are seldom addressed in the literature. The first characteristic is the dynamic properties of the network, in the sense that the topology of the network is constantly changing due to the orbit of each nano-satellite in the constellation.  
The dynamic nature of the network is important in the context of optical communication. This is due to a required acquisition time between two satellites to establish an optical connection. Thus, our suggested network formation method aims to reduce the overall number of required modifications over time.  
The second characteristic addresses the number of peers a satellite can simultaneously communicate with at any given time. This characteristic stems from a dedicated optical module that is mounted on a satellite; a single optical module enables communication with a single peer satellite. Currently, this number is bounded to a small (constant) number of laser modules (commonly 3-5), which is significantly lower than the number of possible \"neighboring\" satellites. Thus, one can model the inter-satellite laser communication problem as a bounded degree spanning network (over dynamic graphs).
## 5 Experiments and Results  
The proposed architecture, ReSVM, was compared with ResNet-50, ResNet-101, VGG-19, MobileNet, InceptionV3, and Xception for a two-category problem of driver's distraction detection. The first category corresponds to normal driving without distraction, while the second corresponds to distracted driving that may include talking on a phone, texting, drinking, operating the radio, talking, combing, and applying makeup. We evaluated the classification accuracy and the execution time of our approach on four publicly available datasets, namely: State Farm Distracted Driving (SFDDD), Boston University (BU), DrivFace, and FT-UMT datasets using 10-fold cross validation.  
The parameter ranges used for ResNet-50, ResNet-50, ResNet-101, VGG-19, MobileNet, InceptionV3, and Xception classifier are dropout = 0.1', activation function ='softmax', and optimizer = 'rmsprop'.
## Materials and Methods  
### Bacterial Cultures and Inoculum Preparation  
A multi-strain mix of _L. monocytogenes_ was used in the study to carry out a challenge test. The mix consisted of an authenticated reference strain (WDCM 00021) and two strains from Istituto Zooprofilattico Sperimentale dell'Umbria and Marche \"Togo Rosati\" (IZSUM) collection isolated from food matrices (Lm15011/14 from salami and Lm36206/14 from dry sausage); each strain was lyophilized and kept at +4 \({}^{\circ}\)C in the IZSUM collection. The inoculum to be used in the challenge study of salami was prepared according to the guidelines of the European Union Reference Laboratory for _Listeria monocytogenes_[31]. Briefly, the bacterial cultures used in the study were regenerated into Brain Heart Infusion (BHI; Oxoid, Basingstoke, UK) by incubation at 37 \({}^{\circ}\)C for 24 h. Aliquots of each activated culture (0.1 ml) were transferred into tubes containing BHI and incubated for 72 h at 10 \(\pm\) 1 \({}^{\circ}\)C, a temperature close to the storage condition of the product. At the end of the incubation time, the cultures were equally combined to form the multi-strain cocktail, which was subsequently centrifuged at 2178 g at 10 \({}^{\circ}\)C for 5 min, the supernatant was discarded and the pellet resuspended in 10 mL of sterile physiological solution. Counts were performed by serial decimal dilution and inoculation in Agar Listeria Ottaviani Agosti (ALOA Selective Supplement, ALOA Enrichment Supplement; Biolife, Italy), incubated at 37 \({}^{\circ}\)C for 24-48 h and the multi-strain suspension was diluted with sterile physiological solution to obtain an inoculum able to determine a final concentration in meat batter of approximately 2 Log Colony Forming Units (CFU)/g.  
Generally, a microbiological challenge test assessing the _Listeria monocytogenes_ growth potential (8) is performed only on final meat products, starting at the day of product contamination and finishing at the end of the shelf life [31]. In this study, the contamination was performed at the beginning of the production process (into meat batter), taking into account the most probable conditions of natural contamination of the product and the specific packaging choices of the manufacturer (the salami is commercialized whole and under vacuum). Therefore, the evaluation of the pathogen's behavior, as well as all the other analytical determinations, were performed during both the production process and the shelf life.
## 3 Detection of Damage Occurrence
### Time Domain Methods
#### 3.1.2 Modern Signal Processing Methods  
In recent years, many signal processing techniques and artificial intelligence as analysis tools have been introduced to investigate the vibration signals and further extract features to represent the signal characteristics. Those methods, such as wavelet analysis, empirical modes decomposition (EMD), and Hilbert-Huang Transform (HHT), are more universal and less dependent on the structural shape. The main concept of using wavelet analysis is to break down the dynamic signal of a structural response into a series of local basis function called wavelets, so as to detect the special characteristics of the structure using scaling and transformation property of wavelets [30; 31; 32; 33]. The method based on the EMD and HHT is intended to extract damage spikes due to a sudden change of structural stiffness from the measured data thereby detecting the damage occurring time instants and locations, and determining the natural frequencies and damping ratios of the structure before and after damage [34]. [PERSON] and [PERSON] [35] developed a recurrence plot-based fault detection method by integrating the statistical process control technique to analyze the vibration signal for damage detection.