esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis and Research on Secondary LT Coding Anti-Eavesdropping Scheme Based on LT Code Degree-1
## 4 Simulation Results and Discussion
### Comparison of the Number of Symbols Sent by the Source  
Under erase channel conditions, fountain codes are used as anti-eavesdropping codes, and the number of symbols sent by the source directly affects the message transmission rate. The scheme double LT of this paper is compared with the literature [21; 22], respectively, to observe the effect of main channel variation on the number of encoded symbols sent by the source.  
As can be seen in Figure 3, the number of encoded symbols sent by the source increases for all four schemes as \(P_{AB}\) increases. The number of double LT is smaller than in the literature [21; 22] and larger than LT codes, respectively.  
Double LT is the result of LT encoding its degree-1 symbols again on the basis of LT codes, and the number of double LT is slightly larger than that of LT codes. In contrast, the literature [21] encodes LT again for length \(\frac{k}{1-P_{AB}}\) encoded symbols, and the literature [22] for length \(\frac{k-n}{1-P_{AB}}\) encoded symbols, which increases the number of encoded symbols required for decoding more substantially. Therefore, comparing the number of encoded symbols sent by the source, double LT is smaller than the literature [21; 22].
## 2 Related Work  
Quantifying the tongue's diagnostic attributes is one of the main challenges in automating tongue analysis for disease diagnosis. Ref. [1] presents a study of tongue conditions based on Ayurveda, which pertain to an individual's health status. Ref. [2] presented a summary of various tongue attributes such as colour coating, texture, and geometric shape to predict specific diseased conditions, followed in oriental medicine. According to TCM (traditional Chinese practice), the best illumination for tongue inspection is sunshine in an open area at 9 am. Artificially, this can be generated with a source with a colour rendering index greater than ninety and a colour temperature around 5000 K. The automation of tongue analysis systems essentially requires an image-capturing device with high-resolution images for accurate extraction of tongue features for disease predictions, in agreement with traditional medicine practitioners. Researchers explore various imaging setups using high-end CCD cameras [3; 4; 5; 6; 7; 8; 9], hyperspectral cameras [10; 11; 12; 13; 14; 15; 16], and smartphone cameras [17; 18; 19; 20; 21; 22; 23; 24]. Before feature extraction and classification, it is necessary to segment the tongue region from the captured images, which include teeth, lips, and skin areas. Over time, researchers have explored conventional approaches, artificial neural networks, and deep learning algorithms for tongue area segmentation and feature extraction. Probably due to the lack of a digital dataset covering all possible features for various diseases, most of the work in this area focused on a specific disease and its associated tongue features. Some common diseases, such as diabetes, appendicitis, and gastritis, were targeted, and relevant features and classification were done using statistical techniques [25; 26; 27; 28]. A hybrid model with statistical methods for feature extraction and machine learning algorithms for classification was also developed by [29; 30; 31; 32; 33]. Table 1 summarises some feature and disease classification methods published. Ref. [47] reviewed current trends in tongue diagnosis. They also trained a classification model using Random Forest and Support Vector Machine on a tongue dataset, with the tongue region divided into five parts as per the layout of the internal organs and extracted seven colour spaces from the five extracted parts. Further, they trained VGG and ResNet pretrained models on tongue constitution classification. ResNet 50 showed the best performance, with 64.52% accuracy. Over the past two decades, enormous efforts have been made to effectively utilise the full potential of the tongue diagnosis system. Enhancement of tools and techniques for achieving the holistic approach and overcoming the subjective nature of diagnosis is an ongoing process, and there is a need to set some standard protocols so that they have uniform characteristics and are readily acceptable by end users. Most of the research has focused on specific diseases and sought to extract tongue features. Refs. [44; 45] achieved notable and accurate results by classifying 12 disease categories, including a healthy tongue,
## 3 Rare Earth Flotation from Primary Resources
### Flotation Reagents  
#### 3.1.1 Collectors  
Between 2012 and 2021, three collector types--carboxylates, hydroxamates, and phosphates/phosphonates--were frequently reported. The typical chemical structures of these three classes of collectors are illustrated in Figure S2. Carboxylates, also known as oxhydryl collectors, are conventional collectors in REM flotation because they are cheaper than the other collector types [31]. The mechanism of carboxylate attachment on REMs can be classified as physisorption and/or chemisorption. [PERSON] et al. [32] proposed that sodium oleate adsorbs on REMs via physisorption when the pulp pH is lower than the point of zero charge (PZC). In contrast, chemisorption was observed to dominate when the pulp pH was higher than the PZC and was attributed to interactions between the negatively charged surface of the REMs and anionic oleate ions [32; 33]. The main disadvantage of carboxylates noted from previous works is their generally low selectivity for REMs because they also tend to adsorb to gangue minerals like calcite, fluorite, dolomite, barite, and quartz [34; 35; 36; 37; 38]. To enhance the REM grade when carboxylate is used, depressants are needed to inhibit collector adsorption to gangue minerals [39] (more details will be provided in the later subsection on depressants). Sodium oleate is the most frequently used carboxylate collector in REM flotation, especially for bastnaesite, monazite, and xenotime (Table 1). The best pH for this collector is around 6-10, where REO recovery of 95% could be obtained when single REMs were evaluated [32; 33; 40; 41]. Unfortunately, this high recovery decreased substantially in the presence of gangue minerals, as highlighted by [PERSON] and [PERSON] [34], who obtained only 32% recovery with monazite-dolomite mixtures. To improve recovery in real REMs, depressants--xanthan gum, lignin sulfonate, sodium silicate, and sodium silicate mixed with starch--are used. Flotation of REMs from low-grade sources using sodium oleate as a collector and sodium silicate as a depressant has reported 40-80% recoveries and obtained 0.3-0.5% grade of products [36; 42; 43; 44; 45]. For REM flotation using fatty acids as collectors, the optimum pH is around 7-11.5. The effects of temperature on REM flotation with fatty acids were evaluated by [PERSON] et al. [46], and they reported negligible improvement in flotation recovery even after increasing the temperature to 40 \({}^{\circ}\)C, due to the low selectivity of this collector at a high temperature. It was also found that, among the fatty acids, Sylfat FA2 was better than Aero 704 at pH 7 to 11.5. Meanwhile, oleic acid, an unsaturated fatty acid, was effectively applied as a collector in REMs with associated silicates and iron-bearing minerals [47].
## 1 Introduction
The approach does not rely on imaging or image processing, which in turn, depends on the resolution of the scan. Furthermore, because of the rotational scanning pattern, we are directly measuring the fiber orientation instead of providing a calculated estimation, the accuracy of which is reflected by the presented error analysis. The presented method allows for accurate 2D visualization and quantification of the fiber's waviness while being flexible in the spatial resolution, scan pattern, and scan region. Because the method measures relative amplitudes at individual points, it is also insensitive to lift-off. This work is an extension of the early study published by [PERSON] et al. [13].
# Diamond-Like Carbon (DLC) Coatings: Classification, Properties, and Applications
## 2 Classification of DLC Coatings
### Structural Arrangement  
Strong C is broadly classified; i.e., ordered (diamond, nanotubes, and graphite) and disordered DLC structures. The structure of formless C comprises sp\({}^{2}\)- and sp\({}^{3}\)-hybridized C. The sp\({}^{3}\)-rich material has 3D organizations with completely covalent bonding. In this way, it is precisely hard and possesses moderately higher density. A density value of more than 3 g/cm\({}^{3}\) is described as a density near the density of diamond [49]. Then again, the sp\({}^{2}\)-rich material has 2D organizations with a covalent bond, a weak bond starting from intermolecular powers. In this way, this structure is precisely delicate and has a moderately low density. This material is indistinct C (a-C), which is infrequently perceived as DLC in a wide range [50]. Hydrogenated C films are additionally utilized for engineering applications.
## 3 Results and Discussion  
The subsequent subsections provide an overview of the performance assessment of the suggested system, encompassing the primary experiments conducted in the laboratory and specifically focusing on the essential metrics. These metrics include the following: (a) the accuracy of the robotic system's motion, (b) the successful identification and localization of plants, and (c) the determination of various morphological attributes of the plants that are used commonly in plant phenotyping applications for plant growth qualification, such as their diameter, as well as contour-related measurements (i.e., perimeter, area, centroid, etc.). It should be noted, however, that while the operational mechanics of the two low-cost distance sensors utilized in this investigation have been previously elucidated, their installation serves the purpose of collecting additional height measurements pertaining to the scene, rather than solely focusing on the height of the plants and so forth. To analyze measurements like lettuce's diameter or area using physical measurement units such as millimeters, it is essential to perform the conversion of pixels to that desired unit. Despite the possibility of obtaining a reliable estimation of the pixel/mm scale in our  
Figure 13: Simplified schematics of delineation metrics.  
experimental scenarios, as already discussed in Sections 2.1.3 and 2.2.4, while illustrated also in Figures 15 and 16 of Appendix A, this is reserved for future endeavors, and all measurements of distance and area in this study are presented in pixels. While this approach does not provide absolute measurements in metric units, it enables the comparative analysis of plants, as they are observed using the same imaging system.  
Programming for the data analysis, regression, and visualization of the results was performed using MatlabTM, ver. 2018b software (The Mathworks, Inc., Natick, MA, USA). The Raspberry Pi 4 of our robotic camera system was running Raspbian GNU/Linux 10 (buster, kernel: 5.10.63), and its programming was performed using Python, release 3.7.3, and OpenCV 4.5.5.
# Decision Framework for Predictive Maintenance Method Selection
## 2 Problem Exploration
### Problem 2--No Proper Fit between the Ambition Level and the Available Data  
To manage their physical assets, practitioners traditionally use fixed maintenance intervals prescribed by the original equipment manufacturers (OEM). As these intervals are not company-specific and are mostly rather conservative, many practitioners feel the need to adapt their intervals to their specific situation and have the ambition to transition to a predictive maintenance approach. A first step would be to use past experiences and historical data as input for stochastic or statistical techniques of failure prediction [26; 27; 28]. Although these traditional methods are widely used, they are limited in effectively predicting the future reliability of individual systems, especially when the use of the system is variable. When the company has the ambition to incorporate these variable operational conditions in the maintenance approach, it is important that this requirement is defined explicitly to ensure the selection of a suitable MM [29]. This is confirmed by [PERSON]'s framework [24], which explicitly includes the company (maintenance) _objectives_, as well as the predictive maintenance _necessity_, in the evaluation.  
Moreover, such an ambition level sets requirements for the data to be used as input for the analyses, as the data completeness and quality significantly affect the outcomes [30]. The input data these models rely on can be imprecise or incomplete, especially when actual failure data are missing due to either bad registration or effective failure prevention. In the case studies, it was observed that the less mature companies struggled with scattered data (e.g., stored in multiple systems, local desktops), with data that are difficult to access (e.g., stored in legacy systems or in text format) or with incomplete data. Note that additionally, the knowledge level of the people involved can be considered part of the data quality, as experience and expert insight is often used in maintenance decision making. [PERSON] [24] specifies both data quality and maintenance employee quality as important aspects of predictive maintenance _capacity_.  
The authors previously demonstrated [10] that the fit between data and the (strived-for) type of analysis seems to be leading for the success of the selected MM. However, this fit is not always checked properly beforehand, which often leads to the previously mentioned trial-and-error processes.
## 3 MEC Network Model  
In this section, we first introduce the system model of a HetMECNet. We characterize the spatial distribution of MEC servers and user devices by employing stochastic geometry. For a comprehensive evaluation of the system's performance, we apply queuing theory to model the task offloading process. A summary of the key notations used throughout this paper are summarized in Table 2.
## 4 Novel Digital Cash Paradigm Description
### No-Valued e-Coin Generation  
A no-valued e-coin is generated by the customer on their own.  
1. The customer generates a simulated message-signature tuple under vendor's public key. Let \(\{Y,\text{Sign}_{PK_{V}}(Y)\}\) be the simulated tuple.
2. The customer generates a random private key \(v_{S}\) and the corresponding public one \(Q_{S}\). The customer also generates a random \(r_{S}\) and computes \((X_{S},Y_{S})=\text{OAEP}_{\text{PA}}(Q_{S},r_{S})\).
3. The customer calculates \(Y_{R}=Y\oplus Y_{S}\), generates a random \(X_{R}\), and computes \((Q_{R},r_{R})=\text{OAEP}^{-1}(X_{R},Y_{R})\). If \(Q_{R}\) is not a valid public key, this step is run again taking a different \(X_{R}\).  
At the end of this process, the customer is in possession of a partial e-coin tuple as that shown in Equation (4).  
\[\{v_{S},Q_{S},(X_{S},Y_{S}),\varnothing,Q_{R},(X_{R},Y_{R}),Y,\text{Sign}_{PK_{ V}}(Y)\}. \tag{4}\]  
The private key \(v_{R}\) corresponding to \(Q_{R}\) is not known, and the corresponding part of the tuple is empty (\(\varnothing\)).  
Note that we need a cryptosystem in which the probability of obtaining a valid public key in a pseudo-random manner is relatively high (step 3). More details are given in Section 5.3.
## 3 Results and Discussion
### SEM and EDX Analysis  
The texture of the as-prepared Pd/C catalyst was scrutinized by SEM analysis (Figure 2), and it was found that the catalysts prepared were found to possess a rugged, undefined morphology, and there was no effect when increasing the Pd upon the carbon support. The images obtained are given in Figure 3. The percentage of composition of Pd in the catalytic system was confirmed by EDX studies, which revealed that the Pd % in the 3% Pd/C catalyst contained 2.88% Pd, which was close to the theoretical percentage.  
Figure 1: XRD analysis of 3% Pd/C.  
Figure 2: SEM images of the 0.5%, 1%, 2% and 3% Pd/C catalysts.
## 1 Introduction  
According to the Food and Agriculture Organization (FAO) of the United Nations, apple is one of the most important and popular fruit species in the World [1, 2, 3], because of its good sensory properties and health benefits [4]. The main apple products are juice, cider, vinegar, and fermented apple juice [5], as well as dehydrated [6; 7; 8; 9], canned [1; 10], and purees [11; 12].  
Nowadays, fermented products, including apple juice, have become very popular because of their health benefits and good sensory properties. Fermentation increases the bioavailability of bioactive substances (polyphenols, etc.) and the nutritional value of the fermentable substrate [13; 14; 15]. Consumers are looking for non-dairy substrates (cereals, vegetables, fruits, etc.) fermented by functional microorganisms (probiotics, possessing antimicrobial properties, etc.) [5]. Non-dairy substrates have more bioactive compounds, are cheaper, and can be used by the lactose-intolerant population [5]. Fermented beverages possess many desirable properties: stronger antioxidant characteristics, gastrointestinal health improvement [16], and possibly possessing antimicrobial properties [17]. These characteristics may be due to technological microorganisms and their metabolites in fermented juice: organic acids, vitamins, phenolic compounds, etc. [18]. However, the characteristics of fermented products depend on the microorganism(s) used, the fermentable substrate, and the fermentation conditions [19; 20; 21].  
Taking into consideration that most (bio)converted (fermented) apple juice products possess desirable health benefits for consumers, in this study, we hypothesized that the combination of acetic, alcoholic, and lactofermented apple juice (AJ) products could have increased value, in comparison with the separate products, because of the greater variety of compounds formed (volatile, antimicrobial, etc.), and could be valuable ingredients for developing nutraceutical chewing candy (NCC) formulations. To select the best combinations, in addition to the standard sensory analysis method, an emotion intensity scanning technique (FaceReader software) was applied in this study, as we hypothesized that the implicit emotional responses revealed through facial expressions could indicate the interaction of consumers with products in a more sensitive manner, because emotions have a significant role in the comprehension of food preferences and consumer acceptability.  
The aim of this study was to develop NCC formulations based on acetic, alcoholic, and lactofermented AJ products. In addition, different texture-forming (gelatin, pectin) and sweetening (stevia, xylitol) agents were tested. First, the most appropriate combination was selected by evaluating the overall acceptability and emotions induced for consumers. The volatile compound profile, and physicochemical and antimicrobial characteristics of the developed combinations were also analyzed. For AJ fermentation, lactic acid bacteria (LAB) strains possessing antimicrobial properties (LUHS122--_L. plantarum_ and LUHS210--_L. casei_) were used.
# D-Limonene: Promising and Sustainable Natural Bioactive Compound
## 4 Antimicrobial Mechanism of D-Limonene
### Antibiofilm Mechanism of D-Limonene
#### 4.2.2 The Inhibition of D-Limonene on Quorum Sensing and Efflux Pump Systems  
The quorum sensing and efflux pump systems are important for biofilm formation and virulence expression [120; 121]. The QS system is a process of microbial communication through small, diffuse chemical signaling molecules (i.e., QS molecules), which can be involved in the coordination of certain behaviors, such as biofilm formation, virulence, and antibiotic resistance [122; 123], and the EP system is responsible for secreting toxins, proteins, and polysaccharides formed by the cell, as well as the efflux of toxic compounds found in the microbial context, such as antibiotics [124]; D-limonene has an inhibitory effect on both systems (Figure 3).  
Autoindducer-2 (AI-2) is a QS signaling molecule commonly shared by gram-positive and gram-negative bacteria that is synthesized by the enzyme LuxS [125] and D-limonene may inhibit _E. coli_ QS by interfering with AI-2 communication; the expression of AI-2-related genes (_lsrA_, _lsrB_, _lsrC_, and _lsrF_) was decreased [74]. Similarly, [PERSON] et al. [126] also reported the inhibitory effect of D-limonene in the production of an autoinducer (AI) in _P. aeruginosa_, whereby D-limonene at 0.8-4 mg/mL reduced the AI production by 17-30%. _Pseudomonas psychrophila_ is one of the main causes of the spoilage of frozen foods [127] and another study treated _P. aeruginosa_ with 135, 65, and 35 \(\mu\)L/mL of D-limonene and showed that bacterial biofilm formation was inhibited and that proteolytic activity and exopolysaccharide synthesis were reduced by 35%, 29%, and 28%, and 58%, 32%, and 41%, respectively. Additionally, the synthesis of QS autoinducers (AHL and alkyl quinolone molecules) was also reduced below detectable levels, indicating that D-limonene effectively inhibited the normal process of QS, and it was found that D-limonene binds to QS receptor proteins (LasR, RhlR, TraR, and PqsR), which may result in inhibition of the normal functioning of the QS system through competitive or non-competitive binding [128]. Inhibition of the EP system significantly inhibited microbial biofilm formation, indicating that the EP system is important for microbial biofilm formation [129]. In the study, it was found that D-limonene suppresses the expression of the EP system-related genes _MrsA_ and _TetK_ of _S. aureus_[130], D-limonene also binds to EP proteins (MuxB, MexB, and Mfs) on the _P. psychrophila_ cell membrane, and it inhibits the expression of the EP system transcriptional regulatory genes _MarR_ and _TetR_[128].  
Figure 3: **(A)** Active quorum sensing; **(B)** Inhibited quorum sensing (Inhibited QS molecule synthesis, transport, and competition for receptors); **(C)** Active efflux pump; **(D)** Inhibited efflux pump (Inhibited substance transport in the EP system).
# Frequency-Bounded Matching Strategy for Wideband LNA Design Utilising a Relaxed SSNM Approach
## 1 Introduction
To validate the proposed methodology, a three-stage monolithic microwave integrated circuit (MMIC) LNA prototype was fabricated using a 0.15 \(\upmu\)m GaAs process, targeting the 28-34 GHz band. The general architecture of this, with various reflection coefficients and mismatches, is shown in Figure 1. Measured results demonstrate a gain of 23 \(\pm\) 1 dB and a noise figure of 2-2.5 dB, closely matching simulations. These results confirm the practical effectiveness of the proposed frequency-bounded mismatch region strategy for wideband LNA design. Circuit schematics and EM simulations are performed using Keysight's Advanced Design System (ADS) software (version 2023 update 1). Figure 1: Detailed block architecture of the three-stage LNA. The remainder of this paper is organised as follows: Section 2 reviews prior approaches to source degeneration and introduces the relaxed SSNM framework. Section 3 presents the concept of frequency-bounded mismatch regions. Section 4 details the proposed design methodology. Section 5 discusses measured results from the fabricated prototype, and Section 6 concludes with insights and future research directions.